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

该项目正在构建全新类型的相机，这些相机将联合收割机结合新颖的光学和算法设计，以克服宏观场景的衍射极限，实现前所未有的图像，深度和材料采集精度。该项目改进了对远处物体图像执行的计算机视觉任务，提高了监控、遥感、机器人导航和自动驾驶汽车等应用的精度。此外，这项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素描的微观表面细节。该研究计划还与综合教育计划紧密结合，包括成像和摄影。该研究小组正在为芝加哥的课后项目开发课程，向高危青少年介绍光学、电子、电动力学和图像处理的基本概念。这项研究的重点是提高对远距离宏观物体成像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。这种新的相干光传输理论甚至包含了视觉外观中最复杂的效果，如参与介质、次表面散射、多次反弹相互反射、衍射和干涉。该研究团队正在开发理论、硬件和算法，从而从根本上改善计算机视觉应用的图像、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，这些相机依赖于相干光学（有源和无源）和新颖算法设计的协同组合，以克服衍射极限。该项目正在构建计算相机，可以解决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.