CGV: Small: A Patch-based Framework for Capturing a World in Motion

虽然真实的世界是动态的，但是用于对其进行成像/捕获的许多技术本质上是顺序的。 例如，在没有特殊硬件的情况下捕获场景的高动态范围（HDR）图像（其包含宽范围的照明）涉及以不同曝光拍摄一组顺序图像，每一个测量小范围的照明。 然而，这种方法在重建具有移动对象的动态场景的HDR图像时存在问题，因为各个帧没有正确配准。 从医学成像到计算机视觉，许多研究领域都存在类似的问题。在这个项目中，PI和他的团队正在开发一个通用框架，用于解决各种不同应用中的运动伪影。 他们的关键见解是，基于补丁的优化可以用于处理运动不一致性，而无需明确解决精确运动估计的挑战性问题。 这产生了以一致的方式从不同输入重建的结果，而没有运动伪影。 PI正在探索如何将该框架应用于几个重要的研究领域，从高质量成像到计算机视觉应用，如动态场景的重建。 改进动态场景的捕获有可能改变某些成像过程（如医学成像）的完成方式。 这项工作的科学成果通过技术出版物在图形/视觉社区的顶级场所传播，PI计划在线发布开发的算法。 最后，这个项目涉及高中和代表性不足的学生到研究工作。

Although the real world is dynamic, many techniques used to image/capture it are fundamentally sequential in nature. For example, capturing a high-dynamic range (HDR) image of a scene (which contains a wide range of illumination) without special hardware involves taking a set of sequential images at different exposures, each one measuring a small range of illumination. However, this approach has problems when reconstructing the HDR image of dynamic scenes with moving subjects, since the individual frames are not registered correctly. Problems like this appear in many research areas, from medical imaging to computer vision.In this project, the PI and his team are developing a common framework that addresses artifacts from motion for a variety of different applications. Their key insight is that patch-based optimization can be used to handle motion inconsistencies without explicitly solving the challenging problem of accurate motion estimation. This produces results that are reconstructed from different inputs in a consistent manner without motion artifacts. The PI is exploring how this framework can be applied to several important research areas, from high-quality imaging to computer vision applications such as the reconstruction of dynamic scenes. Improved capture of dynamic scenes has the potential to transform the way certain imaging procedures (such as medical imaging) are done. Scientific results of this work are disseminated through technical publications at top venues in the graphics/vision communities, and the PI plans to release the algorithms developed online. Finally, this project involves high school and under-represented students into the research effort.