This project is building fundamentally new types of cameras that combine novel optics and algorithm design to overcome the diffraction limit for macroscopic scenes, achieving unprecedented levels of precision in image, depth, and material acquisition. This project improves computer vision tasks performed on images of distant objects, increasing precision for applications such as surveillance, remote sensing, robot navigation, and autonomous vehicles. In addition, the work has direct applications in ongoing art conservation efforts to non-destructively acquire microscopic surface details of paintings and drawings. The research program is also tightly integrated with a comprehensive education program incorporating imaging and photography. The research team is developing curriculum for Chicago afterschool programs to introduce at-risk youth to basic concepts in optics, electronics, electrodynamics, and image processing.
The focus of this research is to increase the level of detail that can be recovered when imaging macroscopic objects at large distances. The research is based on a wave-model of light with computational photography. This new theory of coherent light transport incorporates even the most complex effects of visual appearance such as participating media, sub-surface-scattering, multiple-bounce inter-reflections, diffraction, and interference. The research team is developing theory, hardware, and algorithms that lead to fundamental improvements in image, shape, and material acquisition for computer vision applications. The project is developing fundamentally new types of cameras for computer vision that rely on a synergistic combination of coherent optics (both active and passive) and novel algorithm design to overcome the diffraction limit. This project is constructing computational cameras that can resolve scene details well below the diffraction limit.
