CAREER: Coherent Computational Imaging: Micro Measurements in a Macro World

该项目正在构建新型相机，结合了新颖的光学和算法设计，以克服宏观场景的衍射极限，在图像、深度和材料获取方面达到前所未有的精度水平。该项目改进了在远距离物体图像上执行的计算机视觉任务，提高了监视、遥感、机器人导航和自动驾驶汽车等应用的精度。此外，该工作还直接应用于正在进行的艺术保护工作，以非破坏性地获取绘画和图纸的微观表面细节。研究项目也与综合教育项目紧密结合，包括成像和摄影。研究小组正在为芝加哥的课外活动开发课程，向有危险的青少年介绍光学、电子学、电动力学和图像处理的基本概念。本研究的重点是提高在远距离成像宏观物体时可以恢复的细节水平。这项研究是基于光的波动模型与计算摄影。这种相干光传输的新理论甚至结合了最复杂的视觉外观效应，如参与介质、次表面散射、多次反弹间反射、衍射和干涉。该研究团队正在开发理论、硬件和算法，以从根本上改进计算机视觉应用中的图像、形状和材料获取。该项目正在开发用于计算机视觉的新型相机，它依赖于相干光学（主动和被动）和新型算法设计的协同组合，以克服衍射极限。这个项目正在构建计算相机，它可以分辨出远低于衍射极限的场景细节。

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.