Imaging Science Doctor of Philosophy (Ph.D.) Degree

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Reach the pinnacle of status of higher education in imaging science acquiring the capabilities, skills, and experience to succeed in this diverse field.

Overview for Imaging Science Ph.D.

The Ph.D. in imaging science signifies high achievement in scholarship and independent investigation in the diverse aspects of imaging science. Students contribute their fundamental body of knowledge in science and engineering that is associated with this field of study. As an imaging Ph.D. candidate, you’ll acquire the capabilities, skills, and experience to continue to expand the limits of the discipline and meet future scholarly, industrial, and government demands on the field.

Candidates for the doctoral degree must demonstrate proficiency by:

  • Successfully completing course work, including a core curriculum, as defined by the student’s plan of study;
  • Passing a series of examinations; and
  • Completing an acceptable dissertation under the supervision of the student’s research advisor and dissertation committee.

Plan of Study

All students must complete a minimum of 60 credit hours of course work and research. The core curriculum spans and integrates a common body of knowledge essential to an understanding of imaging processes and applications. Courses are defined by the student’s study plan and must include core course sequences plus a sequence in a topical area such as remote sensing, digital image processing, color imaging, digital graphics, electro-optical imaging systems, and microlithographic imaging technologies.

Students may take a limited number of credit hours in other departments and must complete research credits including two credits of research associated with the research seminar course, Graduate Seminar.

Graduate elective courses offered by the Chester F. Carlson Center for Imaging Science (and other RIT academic departments in fields closely allied with imaging science) allow students to concentrate their studies in a range of imaging science research and imaging application areas, including electro-optical imaging, digital image processing, color science, perception and vision, electrophotography, lithography, remote sensing, medical imaging, electronic printing, and machine vision.

Advancement to Candidacy

Advancement to candidacy occurs through the following steps:

  • Advisor selection
  • Submission and approval of a preliminary study plan
  • Passing a written qualifying exam
  • Study plan revision based on the outcome of qualifying exam and adviser recommendation
  • Research committee appointment
  • Candidacy exam based on thesis proposal

Following the qualifying exam, faculty decide whether a student continues in the doctoral program or if the pursuit of an MS degree or other program option is more acceptable. For students who continue in the doctoral program, the student's plan of study will be revised, a research committee is appointed, candidacy/proposal exams are scheduled, and, finally, a dissertation defense is presented.

Research Committee

Prior to the candidacy exam, the student, in consultation with an advisor, must present a request to the graduate program coordinator for the appointment of a research committee. The committee is composed of at least four people: an advisor, at least one faculty member who is tenured (or tenure-track) and whose primary affiliation is the Carlson Center for Imaging Science (excluding research faculty), a person competent in the field of research who is an RIT faculty member or affiliated with industry or another university and has a doctorate degree, and the external chair. The external chair must be a tenured member of the RIT faculty who is not a faculty member of the center and who is appointed by the dean of graduate education. The committee supervises the student’s research, beginning with a review of the research proposal and concluding with the dissertation defense.

Research Proposal

The student and their research advisor select a research topic for the dissertation. Proposed research must be original and publishable. Although the topic may deal with any aspect of imaging, research is usually concentrated in an area of current interest within the center. The research proposal is presented to the student's research committee during the candidacy exam at least six months prior to the dissertation defense.

Final Examination of the Dissertation

The research advisor, on behalf of the student and the student's research committee, must notify the graduate program coordinator of the scheduling of the final examination of the dissertation by forwarding to the graduate program coordinator the title and abstract of the dissertation and the scheduled date, time, and location of the examination. The final examination of the dissertation may not be scheduled within six months of the date on which the student passed the candidacy exam (at which the thesis proposal was presented and approved).

Barring exceptional circumstances (requiring permission from the graduate program coordinator), the examination may not be scheduled sooner than four weeks after formal announcement (i.e. center-wide hallway postings and email broadcast) has been made of the dissertation title and abstract and the defense date, time, and location.

The final examination of the dissertation is open to the public and is primarily a defense of the dissertation research. The examination consists of an oral presentation by the student, followed by questions from the audience. The research committee may also elect to privately question the candidate following the presentation. The research committee will immediately notify the candidate and the graduate program coordinator of the examination result.'

Residency

All students in the program must spend at least two consecutive semesters (summer excluded) as resident full-time students to be eligible to receive the doctoral degree. If circumstances warrant, the residency requirement may be waived via petition to the graduate program coordinator, who will decide on the student’s petition in consultation with the advisor and graduate faculty. The request must be submitted at least nine months prior to the thesis defense.

Maximum Time Limit

University policy requires that doctoral programs be completed within seven years of the date of the student passing the qualifying exam. Bridge courses are excluded.

All candidates must maintain continuous enrollment during the research phase of the program. Such enrollment is not limited by the maximum number of research credits that apply to the degree. Normally, full-time students complete the course of study for the doctorate in approximately three to five years. A total of seven years is allowed to complete the degree after passing the qualifying exam.

National Labs Career Fair

Hosted by RIT’s Office of Career Services and Cooperative Education, the National Labs Career Fair is an annual event that brings representatives to campus from the United States’ federally funded research and development labs. These national labs focus on scientific discovery, clean energy development, national security, technology advancements, and more. Students are invited to attend the career fair to network with lab professionals, learn about opportunities, and interview for co-ops, internships, research positions, and full-time employment.

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Research

The College of Science consistently receives research grant awards from organizations that include the National Science Foundation, National Institutes of Health, and NASA, which provide you with unique opportunities to conduct cutting-edge research with faculty. Faculty from the Chester F. Carlson Center for Imaging Science conduct research on a broad variety of topics including:

  • astronomy
  • cultural heritage imaging
  • detectors and imaging systems
  • human and computer vision
  • remote sensing
  • nanoimaging
  • magnetic resonance
  • optical imaging

Learn more by exploring the Carlson Center's imaging science research areas.

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Curriculum for 2024-2025 for Imaging Science Ph.D.

Current Students: See Curriculum Requirements

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Imaging Science, Ph.D. degree, typical course sequence

Course Sem. Cr. Hrs.
First Year
IMGS-606
Graduate Seminar I
This course is focused on familiarizing students with research activities in the Carlson Center, research practices in the university, research environment and policies and procedures impacting graduate students. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course also addresses issues and practices associated with technical presentation and technical writing. Credits earned in this course apply to research requirements. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Seminar 1 (Fall).
 1
IMGS-607
Graduate Seminar II
This course is a continuation of the topics addressed in the preceding course Imaging Science Graduate Seminar I. The course is coupled with the research seminar sponsored by the Center for Imaging Science (usually weekly presentations). Students are expected to attend and participate in the seminar as part of the course. The course addresses issues and practices associated with technical presentations. Credits earned in this course apply to research requirements. (Prerequisites: IMGS-606 or equivalent course.) Seminar 1 (Spring).
 1
IMGS-609
Graduate Laboratory I
This course is the first semester course of a two-semester sequence providing foundational skills in computer programming required in the field of Imaging Science. This course is focused on mastery of fundamental of Python and c++ computer programming skills and their application to problems in Imaging Science. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4 (Fall).
 2
IMGS-613
Noise and Systems Modeling
This course develops models of noise and random processes within the context of imaging systems. The focus will be on stationary random processes in the spatial and spatial frequency domain. The concept of image noise is introduced in both the analog and digital domain. Random processes are studied in both the spatial and spatial frequency domain stressing the autocorrelation function and the power density spectrum. The application of random processes to the understanding of signal noise in imaging systems in both the continuous and the digital domains is presented. Tools for modeling signal and noise transfer are emphasized. At the completion of the course the student should have the ability to model signal and noise transfer within a multistage imaging system. (Prerequisites: IMGS-617 or equivalent course.) Lecture 2 (Spring).
 2
IMGS-617
Image Processing and Discrete Fourier Methods
This course considers sampled and quantized images and temporal image sequences, along with methods for performing useful image processing. These processing methods are classified based on the number of input picture elements (“pixels”) that determine the value of each output pixel: single pixels, local neighborhoods, or global operations. The discrete Fourier transform is introduced. Application to image segmentation and compression are considered. Lecture 2 (Fall).
 2
IMGS-619
Radiometry
This course is focused on the fundamentals of radiation propagation as it relates to making quantitative measurements with imaging systems. The course includes an introduction to common radiometric terms, detector figures of merit, and noise concepts. (This course is restricted to Graduate students.) Lecture 2 (Fall).
 2
IMGS-620
The Human Visual System
This course describes the underlying structure of the human visual system, the performance of those structures and the system as a whole, and introduces psychophysical techniques used to measure them. The visual system's optical neural systems responsible for collecting and detecting spatial, temporal, and spectral signals from the environment are described. The sources and extent of limitations in the subsystems are described and discussed in terms of the "enabling limitations" that allow practical imaging systems. (This course is restricted to Graduate students.) Lecture 2 (Fall).
 2
IMGS-621
Computer Vision
This course will cover a wide range of current topics in modern image processing and computer vision. Topics will include introductory concepts in supervised and unsupervised machine learning, linear and nonlinear filtering, image enhancement, supervised and unsupervised image segmentation, object classification, object detection, feature matching, image registration, and the geometry of cameras. Assignments will involve advanced computational implementations of selected topics from the current literature in a high-level language such as Python, MATLAB, or Julia and will be summarized by the students in written technical papers. The course requires computer programming, linear algebra, and calculus. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 2 (Spring).
 2
IMGS-633
Optics for Imaging
This course describes Fourier transform of continuous functions, followed by its application to describe optical imaging systems in the wave model, including the concepts of point spread function, optical transfer function, and image resolution. Analysis of optical imaging systems using the ray model for systems composed of one thick lens and two thin lenses are considered. (Prerequisites: IMGS-617 or equivalent course.) Lecture 2 (Spring).
 2
IMGS-890
Research & Thesis*
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 4
Second Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 6
 
IMGS Electives*
 12
Third Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 6
 
IMGS Electives*
 6
Fourth Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 6
Fifth Year
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 4
Total Semester Credit Hours
60


 

Electives

Course Sem. Cr. Hrs.
ASTP-613
Astronomical Observational Techniques and Instrumentation
This course will survey multi-wavelength astronomical observing techniques and instrumentation. The design characteristics and function of telescopes, detectors, and instrumentation in use at the major ground based and space based observatories will be discussed as will common observing techniques such as imaging, photometry and spectroscopy. The principles of cosmic ray, neutrino, and gravitational wave astronomy will also be briefly reviewed. (Prerequisites: This course is restricted to students in the ASTP-MS and ASTP-PHD programs.) Lecture 3 (Fall).
 3
CLRS-601
Principles of Color Science
This course covers the principles of color science including theory, application, and hands-on experience incorporated into the lectures. Topics include color appearance (hue, lightness, brightness, chroma, saturation, colorfulness), colorimetry (spectral, XYZ, xyY, L*a*b*, L*C*abhab, ΔE*ab, ΔE00), the use of linear algebra in color science and color imaging, metamerism, chromatic adaptation, color inconstancy, color rendering, color appearance models (CIECAM02), and image appearance models (S-CIELAB, iCAM). (Prerequisites: Graduate standing in CLRS-MS, IMGS-MS, CLRS-PHD or IMGS-PHD.) Lecture 3 (Fall).
 3
CLRS-602
Color Physics and Applications
This course explores the relationship between a material’s color and its constituent raw materials such as colorants, binding media, substrates, and overcoats. These can be determined using a variety of physical models based on absorption, scattering, luminescence, and interference phenomena. These models enable the production of paints, plastics, colored paper, printing, and others to have specific colors. Accompanying laboratories will implement and optimize these models using filters, artist opaque and translucent paints and varnishes including metallic and pearlescent colorants, and inkjet printing. Statistical techniques include principal component analysis and linear and nonlinear optimization. (Prerequisites: CLRS-601 or equivalent course.) Lecture 3 (Spring).
 3
CLRS-720
Computational Vision Science
Computational Vision Science This course provides an introduction to modern computer-based methods for the measurement and modeling of human vision. Lectures will introduce the experimental techniques of visual psychophysics including threshold measurement, psychometric functions, signal detection theory, and indirect, direct, and multidimensional scaling. Lectures will also introduce the MATLAB technical computing environment and will teach how to use MATLAB to run computer-based psychophysical experiments and to analyze experimental data and visualize results. Laboratory exercises will provide practical experience in using computer-based tools to conduct psychophysical experiments and to develop computational models of the results. Prior experience in vision science and/or scientific computing will be helpful but is not required. (Prerequisites: Graduate standing in CLRS-MS, IMGS-MS, CLRS-PHD or IMGS-PHD.) Lecture 3 (Fall).
 3
CLRS-820
Modeling Visual Perception
This course presents the transition from the measurement of color matches and differences to the description and measurement of color appearance in complex visual stimuli. This seminar course is based mainly on review and student-led discussion of primary references. Topics include: appearance terminology, appearance phenomena, viewing conditions, chromatic adaptation, color appearance modeling, image appearance, image quality, and material appearance. (Prerequisites: CRLS-601 and CLRS-720 or equivalent courses.) Lecture 3 (Spring).
 3
CSCI-603
Computational Problem Solving
This course focuses on the application of computational thinking using a problem-centered approach. Specific topics include: expression of algorithms in pseudo-code and a programming language; elementary data structures such as lists, trees and graphs; problem solving using recursion; and debugging and testing. Assignments (both in class and homework) requiring a pseudo-code solution and implementation in a programming language are an integral part of the course. Note: This course serves as a bridge course for graduate students and cannot be taken by undergraduate students without permission from the CS Undergraduate Program Coordinator. (This course is restricted to students in COMPSCI-MS.) Lecture 3 (Fall, Spring).
 3
CSCI-630
Foundations of Artificial Intelligence
An introduction to the theories and algorithms used to create artificial intelligence (AI) systems. Topics include search algorithms, logic, planning, machine learning, and applications from areas such as computer vision, robotics, and natural language processing. Programming assignments and oral/written summaries of research papers are required. (Prerequisites:((CSCI-603 or CSCI-605) &CSCI-661) with grades of B or better or ((CSCI-243 or SWEN-262)&(CSCI-262 or CSCI-263)).If you have earned credit for CSCI-331 or you are currently enrolled in CSCI-331 you won't be permitted to enroll in CSCI-630.) Lecture 3 (Fall, Spring).
 3
CSCI-631
Foundations of Computer Vision
An introduction to the underlying concepts of computer vision and image understanding. The course will consider fundamental topics, including image formation, edge detection, texture analysis, color, segmentation, shape analysis, detection of objects in images and high level image representation. Depending on the interest of the class, more advanced topics will be covered, such as image database retrieval or robotic vision. Programming assignments are an integral part of the course. Note: students who complete CSCI-431 may not take CSCI-631 for credit. (Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.Prerequisites:(CSCI-603 and CSCI-605 and CSCI-661 with grades of B or better) or ((CSCI-243 or SWEN-262) and (CSCI-262 or CSCI-263)) or equiv courses. If earned credit for/or currently enrolled in CSCI-431 you will not be permitted to enroll in CSCI-631.) Lecture 3 (Fall, Spring).
 3
EEEE-780
Digital Video Processing
In this graduate level course the following topics will be covered: Representation of digital video - introduction and fundamentals; Time-varying image formation models including motion models and geometric image formation; Spatio-temporal sampling including sampling of analog and digital video; two dimensional rectangular and periodic Sampling; sampling of 3-D structures, and reconstruction from samples; Sampling structure conversion including sampling rate change and sampling lattice conversion; Two-dimensional motion estimation including optical flow based methods, block-based methods, Pel-recursive methods, Bayesian methods based on Gibbs Random Fields; Three-dimensional motion estimation and segmentation including methods using point correspondences, optical flow & direct methods, motion segmentation, and stereo and motion tracking. (Prerequisites: EEEE-779 or equivalent course.) Lecture 3 (Spring).
 3
ENVS-650
Hydrologic Applications of Geographic Information Systems
Aerial photography, satellite imagery, Global Positioning Systems (GPS), and Geographic Information Systems (GIS) are extremely useful tools in hydrologic modeling and environmental applications such as rainfall runoff modeling, pollution loading, landscape change analyses, and terrain modeling. This course will: 1) introduce students to spatial analysis theories, techniques and issues associated with hydrologic and environmental applications; 2) provide hands-on training in the use of these spatial tools and models while addressing a real problem; 3) provide experience linking GIS and model results to field assessments and monitoring activities; 4) enable students to solve a variety of spatial and temporal hydrologic and environmental problems; and 5) provide tools useful for addressing environmental problems related to the graduate thesis or project. (Prerequisites: ENVS-250 or equivalent course or graduate standing in the ENVS-MS program.) Lec/Lab 6 (Spring).
 4
IMGS-622
Vision Sciences Seminar
This seminar course provides a forum in which students, faculty, and researchers with an interest in the Vision Sciences (visual neuroscience, perception psychology, computational vision, computer graphics) can interact through reading, presentation, and discussion of classic texts and contemporary research papers in the field. Students will read and summarize weekly readings in writing and will periodically prepare presentations and lead discussions. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 1 (Fall, Spring).
 1
IMGS-624
Interactive Virtual Env
This course provides experience in the development of real-time interactive three-dimensional environments, and in the use of peripherals, including virtual reality helmets, motion tracking, and eye tracking in virtual reality. Students will develop expertise with a contemporary Game Engine, along with an understanding of the computations that facilitate 3D rendering for interactive environments. Projects will cover topics such as lighting and appearance modelling, mathematics for vertex manipulation, 3D to 2D projection, ray tracing, the integration of peripherals via software development kits, and the spatial and temporal calibration of an eye tracker embedded within a head-worn display. Students will complete homework tutorials on game/application development in a contemporary computer gaming engine. This course involves a substantial programming component, and prior programming experience is required. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lab 4, Lecture 1 (Fall).
 3
IMGS-628
Design and Fabrication of Solid State Cameras
The purpose of this course is to provide the student with hands-on experience in building a CCD camera. The course provides the basics of CCD operation including an overview, CCD clocking, analog output circuitry, cooling, and evaluation criteria. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lab 6, Lecture 1 (Fall).
 3
IMGS-632
Advanced Environmental Applications of Remote Sensing
This course will focus on a broader selection of analytical techniques with an application-centric presentation. These techniques include narrow-band indices, filtering in the spatial and frequency domains, principal component analysis, textural analysis, hybrid and object-oriented classifiers, change detection methods, and structural analysis. All of these techniques are applied to assessment of natural resources. Sensing modalities include imaging spectroscopy (hyperspectral), multispectral, and light detection and ranging (lidar) sensors. Applications such as vegetation stress assessment, foliar biochemistry, advanced image classification for land use purposes, detecting change between image scenes, and assessing topography and structure in forestry and grassland ecosystems (volume, biomass, biodiversity) and built environments will be examined. Real-world remote sensing and field data from international, US, and local sources are used throughout this course. Students will be expected to perform a more comprehensive final project and homework assignments, including literature review and discussion and interpretation of results. (This course requires permission of the Instructor to enroll.) Lab 3, Lecture 2 (Spring).
 3
IMGS-635
Optical System Design and Analysis
The primary objectives of this course are to teach critical optics and system concepts, and skills to specify, design, simulate, and evaluate optical components and systems. A modern optical design program and various types of optical systems will be used to illustrate how to solve real-world optical engineering problems. The course is not a traditional lens design course, which usually focuses on designing and optimizing individual lens elements. Instead the course will emphasize analyzing systems, which are often made with off-the-shelf optical components. (Prerequisites: IMGS-321 or IMGS-633 or (EEEE-505 and EEEE-705) or (IMGS-322 or PHYS-365) or equivalent course.) Lecture 1 (Spring).
 3
IMGS-639
Principles of Solid State Imaging Arrays
This course covers the basics of solid state physics, electrical engineering, linear systems and imaging needed to understand modern focal plane array design and use. The course emphasizes knowledge of the working of CMOS and infrared arrays. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
 3
IMGS-640
Remote Sensing Systems and Image Analysis
This course introduces the students to the governing equations for radiance reaching aerial or satellite based imaging systems. It then covers the temporal, geometric, spectral, and noise properties of these imaging systems with an emphasis on their use as quantitative scientific instruments. This is followed by a treatment of methods to invert the remotely sensed image data to measurements of the Earth’s surface (e.g. reflectance and temperature) through various means of inverting the governing radiometric equation. The emphasis is on practical implementation of multidimensional image analysis and examining the processes governing spatial, spectral and radiometric image fidelity. (Prerequisite: IMGS-251 or equivalent course.) Lecture 3 (Fall).
 3
IMGS-642
Testing of Focal Plane Arrays
This course is an introduction to the techniques used for the testing of solid state imaging detectors such as CCDs, CMOS and Infrared Arrays. Focal plane array users in industry, government and university need to ensure that key operating parameters for such devices either fall within an operating range or that the limitation to the performance is understood. This is a hands-on course where the students will measure the performance parameters of a particular camera in detail. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lab 6, Lecture 1 (Spring).
 3
IMGS-643
Mathematical Methods of Imaging Science 1
This course will provide the foundational mathematics needed in Imaging Science. This course is the first semester in a two-semester sequence covering fundamental mathematical tools and methods with specific examples drawn from Imaging Science. Students will have the opportunity to put concepts into practice through practical implementation in computer programming assignments. (This course is restricted to Graduate students.) Lecture 1 (Fall).
 1
IMGS-644
Mathematical Methods of Imaging Science 2
(This course is restricted to Graduate students.) Lecture 1 (Spring).
 1
IMGS-684
Deep Learning for Vision
This course will review neural networks and related theory in machine learning that is needed to understand how deep learning algorithms work. The course will include the latest algorithms that use deep learning to solve problems in computer vision and machine perception, and students will read recent papers on these systems. Students will implement and evaluate one or more of these systems and apply them to problems that match their interests. Students are expected to have taken multiple computer programming courses and to be comfortable with linear algebra and calculus. No prior background in machine learning or pattern recognition is required. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
 3
IMGS-689
Graduate Special Topics
This course is a faculty-developed exploration of appropriate graduate-level imaging topics that are not part existing courses. The level of study is appropriate for upper-class undergraduates or graduate level students. Lecture 3 (Fall, Spring, Summer).
 1-3
IMGS-699
Imaging Science Graduate Co-op
This course is a cooperative education experience for graduate imaging science students. CO OP (Fall, Spring, Summer).
 0
IMGS-712
Multi-view Imaging
Images are 2D projections gathered from scenes by perspective projection. By making use of multiple images it is possible to construct 3D models of the scene geometry and of objects in the scene. The ability to derive representations of 3D scenes from 2D observations is a fundamental requirement for applications in robotics, intelligence, medicine and computer graphics. This course develops the mathematical and computational approaches to modeling of 3D scenes from multiple 2D views. After completion of this course students are prepared to use the techniques in independent research. (Prerequisites: IMGS-616 or IMGS-682 or equivalent course.) Lecture 3 (Spring).
 3
IMGS-719
Radiative Transfer I
This course is the first course in a two-semester course sequence that covers the theory of radiative transfer in disordered media. The course begins with a brief review of basic electromagnetism and models for scattering and absorption by single particles and progresses to the theory of radiative transfer in semi-infinite media. Various approximations that allow closed-form solutions are presented, and related phenomenology, such as the shadow-hiding opposition effect and coherent backscatter opposition effects, are described in terms of these models. (Prerequisites: IMGS-619 and IMGS-633 or ASTP-615 or equivalent courses.) Lecture 3 (Spring).
 3
IMGS-720
Radiative Transfer II
This course covers advanced topics related to the theory of radiative transfer in disordered media. The course begins with a review of topics presented in the first semester course, including the radiative transfer solutions due to Hapke’s solution for a semi-infinite medium and the opposition effect. Students will complete a project focused on one or more advanced topics related to radiative transfer in disordered media, such as effects of surface roughness, scattering in layered media, oriented scattering layers, more advanced treatments of multiple scattering or polarization, or radiative transfer in the water column. (Prerequisites: IMGS-719 or equivalent course.) Lecture 3 (Spring).
 3
IMGS-723
Remote Sensing: Spectral Image Analysis
This course is focused on analysis of high-dimensional remotely sensed data sets. It begins with a review of the properties of matter that control the spectral nature of reflected and emitted energy. It then introduces mathematical ways to characterize spectral data and methods to perform initial analysis of spectral data to characterize and preprocess the data. These include noise characterization and mitigation, radiometric calibration, atmospheric compensation, dimensionality characterization, and reduction. Much of the course focuses on spectral image analysis algorithms employing conceptual approaches to characterizing the data. These analytical tools are aimed at segmentation, subpixel or pixel unmixing approaches and target detection including treatment of signal processing theory and application. There is also a significant emphasis on incorporation of physics-based algorithms into spectral image analysis. The course concludes with an end-to-end treatment of image fidelity incorporating atmospheres, sensors, and image processing effects. (Prerequisites: IMGS-619 or equivalent course.) Lecture 3 (Fall).
 3
IMGS-724
Introduction to Electron Microscopy
The course will introduce the basic concepts and practice of electron microscopy, including transmission electron microscopy (TEM), scanning electron microscopy (SEM) and x-ray microanalysis. During the second half of the course students will do an 8-10 hour hands-on project in SEM or TEM or both, including a project paper and a poster presentation. Laboratory demonstrations will be held in the NanoImaging Lab to reinforce the lecture material. (This course is restricted to students with graduate standing in the College of Science or the Kate Gleason College of Engineering.) Lecture 3 (Spring).
 3
IMGS-730
Magnetic Resonance Imaging
This course is designed to teach the principles of the imaging technique called magnetic resonance imaging (MRI). The course covers spin physics, Fourier transforms, basic imaging principles, Fourier imaging, imaging hardware, imaging techniques, image processing, image artifacts, safety, and advanced imaging techniques. (This class is restricted to graduate students in the IMGS-MS or IMGS-PHD programs.) Lecture 3 (Spring).
 3
IMGS-732
Synthetic Aperture Radar Image Formation Processing
This course covers the history and fundamental principles of synthetic aperture radar image formation processing (SAR-IFP). Topics included a review of linear systems theory, an introduction to radar systems, synthetic aperture radar (SAR) concepts, exposure to commonly used spotlight and stripmap image formation algorithms, and autofocus. The course concludes with a discussion on linear frequency modulated continuous wave (LFM-CW) SAR systems that are often fielded on very small, low-cost, low power platforms. Spotlight mode image formation algorithms covered in this course include the Polar Formatting Algorithm (PFA) and the Backprojection Algorithm. Stripmap mode image formation algorithms addressed include the Range-Doppler Algorithm and the Omega-K Algorithm. Along the way, a variety of remote sensing and linear systems theory will be employed to provide specific insight into the following system performance metrics: image size, area rate, resolution, impulse response, noise equivalent backscatter coefficient, residual quadratic phase error, depth of focus, and geometric distortion. (Prerequisites: IMGS-261 or IMGS-616 or IMGS-617 or equivalent course.) Lecture 3 (Fall).
 3
IMGS-740
Imaging Science MS Systems Project Paper
The analysis and solution of imaging science systems problems for students enrolled in the MS Project capstone paper option. Research 3 (Fall, Spring, Summer).
 3
IMGS-765
Performance Modeling and Characterization of Remote Sensing System
This course introduces the techniques utilized for system performance predictions of new imaging platforms during their design phase. Emphasis will be placed on systems engineering concepts and their impact on final product quality through first principles modeling. In addition, the student will learn techniques to characterize system performance during actual operation to monitor compliance to performance specifications and monitor system health. Although the focus of the course will be on electro-optical collection systems, some modality specific concepts will be introduced for LIDAR, broadband infrared, polarimetric, and hyperspectral systems. (Prerequisites: IMGS-616 and IMGS-619 or equivalent courses.) Lecture 3 (Spring).
 3
IMGS-766
Geometric Optics and Lens Design
This course leads to a thorough understanding of the geometrical properties of optical imaging systems and detailed procedures for designing any major lens system. Automatic lens design, merit functions, and optimization are applied to real design problems. The course will utilize a modern optical design program and examples carried out on a number of types of lenses to illustrate how the process of design is carried out. (Prerequisites: IMGS-633 or equivalent course.) Lab 2, Lecture 2 (Fall).
 3
IMGS-789
Graduate Special Topics: Robot Vision
This is a graduate-level course on a topic that is not part of the formal curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lec/Lab (Fall, Spring, Summer).
 1-3
IMGS-789
Graduate Special Topics: Machine Learning for Difficult Data
This is a graduate-level course on a topic that is not part of the formal curriculum. This course is structured as an ordinary course and has specific prerequisites, contact hours, and examination procedures. (This class is restricted to degree-seeking graduate students or those with permission from instructor.) Lec/Lab (Fall, Spring, Summer).
 3
IMGS-790
Research & Thesis
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 1-6
IMGS-799
Imaging Science Independent Study
This course is a faculty-directed tutorial of appropriate topics that are not part of the formal curriculum. The level of study is appropriate for student in their graduate studies. (Enrollment in this course requires permission from the department offering the course.) Ind Study (Fall, Spring, Summer).
 1-4
IMGS-830
Advanced Topics in Remote Sensing
This course is an in-depth examination of emerging techniques and technologies in the field of remote sensing at an advanced level. Examples of topics, which will differ each semester, are typically formed around a specific remote sensing modality such as lidar, polarimetry, radar, and hyperspectral remote sensing. (Prerequisites: IMGS-723 or equivalent course.) Lecture 3 (Spring).
 3
IMGS-890
Research & Thesis
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor. (Enrollment in this course requires permission from the department offering the course.) Thesis (Fall, Spring, Summer).
 1-6
MATH-605
Stochastic Processes
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows. (Prerequisites: ((MATH-241 or MATH-241H) and MATH-251) or equivalent courses or graduate standing in ACMTH-MS or MATHML-PHD or APPSTAT-MS programs.) Lecture 3 (Spring).
 3
MATH-645
Graph Theory
This course introduces the fundamental concepts of graph theory. Topics to be studied include graph isomorphism, trees, network flows, connectivity in graphs, matchings, graph colorings, and planar graphs. Applications such as traffic routing and scheduling problems will be considered. (This course is restricted to students with graduate standing in the College of Science or Graduate Computing and Information Sciences.) Lecture 3 (Fall).
 3
MCSE-712
Nonlinear Optics
This course introduces nonlinear concepts applied to the field of optics. Students learn how materials respond to high intensity electric fields and how the materials response: enables the generation of other frequencies, can focus light to the point of breakdown or create waves that do not disperse in time or space solitons, and how atoms can be cooled to absolute zero using a(laser. Students will be exposed to many applications of nonlinear concepts and to some current research subjects, especially at the nanoscale. Students will also observe several nonlinear-optical experiments in a state-of-the-art photonics laboratory. (Prerequisites: EEEE-374 or equivalent course or graduate student standing in the MCSE-PHD program.) Lecture 3 (Spring).
 3
MCSE-713
Lasers
This course introduces students to the design, operation and (applications of lasers (Light Amplification by Stimulated Emission of (Radiation). Topics: Ray tracing, Gaussian beams, Optical cavities, (Atomic radiation, Laser oscillation and amplification, Mode locking and Q switching, and Applications of lasers. (Prerequisites: EEEE-374 or equivalent course or graduate student standing in the MCSE-PHD program.) Lecture 3 (Fall).
 3
MCSE-731
Integrated Optical Devices & Systems
This course discusses basic goals, principles and techniques of integrated optical devices and systems, and explains how the various optoelectronic devices of an integrated optical system operate and how they are integrated into a system. Emphasis in this course will be on planar passive optical devices. Topics include optical waveguides, optical couplers, micro-optical resonators, surface plasmons, photonic crystals, modulators, design tools and fabrication techniques, and the applications of optical integrated circuits. Some of the current state-of-the-art devices and systems will be investigated by reference to journal articles. Lecture 3 (Fall).
 3
STAT-641
Applied Linear Models - Regression
A course that studies how a response variable is related to a set of predictor variables. Regression techniques provide a foundation for the analysis of observational data and provide insight into the analysis of data from designed experiments. Topics include happenstance data versus designed experiments, simple linear regression, the matrix approach to simple and multiple linear regression, analysis of residuals, transformations, weighted least squares, polynomial models, influence diagnostics, dummy variables, selection of best linear models, nonlinear estimation, and model building. (This class is restricted to students in the APPSTAT-MS, SMPPI-ACT, or APPSTAT-U programs.) Lecture 3 (Fall, Spring, Summer).
 3
STAT-758
Multivariate Statistics for Imaging Science
This course introduces multivariate statistical techniques and shows how they are applied in the field of Imaging Science. The emphasis is on practical applications, and all topics will include case studies from imaging science. Topics include experimental design and analysis, the multivariate Gaussian distribution, principal components analysis, singular value decomposition, orthogonal subspace projection, cluster analysis, canonical correlation and canonical correlation regression, regression, multivariate noise whitening. This course is not intended for CQAS students unless they have particular interest in imaging science. CQAS students should be taking the course STAT-756-Multivariate Analysis. (Prerequisites: This class is restricted to students in APPSTAT-MS, SMPPI-ACT, IMGS-MS, IMGS-PHD, CLRS-MS or CLRS-PHD.) Lecture 3 (Summer).
 3

Admissions and Financial Aid

This program is available on-campus only.

Offered Admit Term(s) Application Deadline STEM Designated
Full‑time Fall January 15 priority deadline, rolling thereafter Yes

Full-time study is 9+ semester credit hours. International students requiring a visa to study at the RIT Rochester campus must study full‑time.

Application Details

To be considered for admission to the Imaging Science Ph.D. program, candidates must fulfill the following requirements:

  • Learn tips to apply for a doctoral program and then complete a graduate application.
  • Submit copies of official transcript(s) (in English) of all previously completed undergraduate and graduate course work, including any transfer credit earned.
  • Hold a baccalaureate degree (or US equivalent) from an accredited university or college in the physical sciences, mathematics, computer science, or engineering. A minimum cumulative GPA of 3.0 (or equivalent) is recommended.
  • Submit a current resume or curriculum vitae.
  • Submit a statement of purpose for research which will allow the Admissions Committee to learn the most about you as a prospective researcher.
  • Submit two letters of recommendation.
  • Entrance exam requirements: GRE optional but recommended. No minimum score requirement.
  • Submit English language test scores (TOEFL, IELTS, PTE Academic), if required. Details are below.

English Language Test Scores

International applicants whose native language is not English must submit one of the following official English language test scores. Some international applicants may be considered for an English test requirement waiver.

TOEFL IELTS PTE Academic
100 7.0 70

International students below the minimum requirement may be considered for conditional admission. Each program requires balanced sub-scores when determining an applicant’s need for additional English language courses.

How to Apply Start or Manage Your Application

Cost and Financial Aid

An RIT graduate degree is an investment with lifelong returns. Ph.D. students typically receive full tuition and an RIT Graduate Assistantship that will consist of a research assistantship (stipend) or a teaching assistantship (salary).

Contact

Admissions Contact
  • Laura Watts
  • Senior Associate Director
  • Office of Graduate and Part-Time Enrollment Services
  • Enrollment Management
  • 585‑475‑4901
  • Laura.Watts@rit.edu
Program Contact
Offered within the
Chester F. Carlson Center for Imaging Science