Inverse Problems in Imaging
ApplyProject Description
Inverse problems are abundant in the field of imaging, and range from simple image processing tasks such as denoising and deblurring to full-scale reconstruction problems like computed tomography (reconstructing 3D volumes from 2D projections). The purpose of this internship is to learn about inverse problems, and critical techniques for solving them, including convex and non-convex optimization, sparse coding, and compressive sensing.
Program -
Computer Science
Division -
Computer, Electrical and Mathematical Sciences and Engineering
Center Affiliation -
Visual Computing Center
Field of Study -
Computer Science, Applied Mathematics.
About the
Researcher
Wolfgang Heidrich
Professor, Computer Science
Professor Heidrich's core research interests are in computational photography and display, an emerging research area within visual computing, which combines methods from computer graphics, machine vision, imaging, inverse methods, optics and perception to develop new sensing and display technologies.
Computational photography aims to develop new cameras and imaging modalities that optically encode information about the real world in such a way that it can be captured by image sensors. The resulting images represent detailed information such as scene geometry, motion of solids and liquids, multi-spectral information, or high contrast (high dynamic range), which can then be computationally decoded using inverse methods, machine learning, and numerical optimization.
Computational displays use a similar approach, but in reverse. Here, the goal is to computationally encode a target image that is then optically decoded by the display hardware for presentation to a human observer. Computational displays are capable of generating glasses-free 3D displays, high dynamic range imagery, or images and videos with spatial and/or temporal super-resolution.
Computational photography aims to develop new cameras and imaging modalities that optically encode information about the real world in such a way that it can be captured by image sensors. The resulting images represent detailed information such as scene geometry, motion of solids and liquids, multi-spectral information, or high contrast (high dynamic range), which can then be computationally decoded using inverse methods, machine learning, and numerical optimization.
Computational displays use a similar approach, but in reverse. Here, the goal is to computationally encode a target image that is then optically decoded by the display hardware for presentation to a human observer. Computational displays are capable of generating glasses-free 3D displays, high dynamic range imagery, or images and videos with spatial and/or temporal super-resolution.
Desired Project Deliverables
This project requires some familiarity with basic numerical methods as well as programming skills. Close collaboration with other team members is expected. Possibility for co-authoring a scientific article in a conference or journal.