Learning Spatially Varying Pixel Exposures for Motion Deblurring
Learning Spatially Varying Pixel Exposures for Motion Deblurring
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DOI：
10.1109/iccp54855.2022.9887786
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Cindy M. Nguyen;Julien N. P. Martel;Gordon Wetzstein
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文献类型：
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作者：
Cindy M. Nguyen;Julien N. P. Martel;Gordon Wetzstein
在计算摄影中，通过计算消除由相机抖动或物体运动引起的运动模糊仍然是一项具有挑战性的任务。去模糊方法通常受到图像捕获过程中固定的全局曝光时间的限制。后处理算法要么必须去除含有相对较少噪声的较长曝光的模糊，要么以增加噪声为代价有意消除模糊的短曝光的去噪。我们提出了一种利用空间变化像素曝光进行运动去模糊的新方法，该方法使用下一代焦平面传感器处理器以及这些曝光的端到端设计和基于机器学习的运动去模糊框架。我们在模拟和物理原型中证明，学习空间变化像素曝光（L-SVPE）可以在恢复高频细节的同时成功地去模糊场景。我们的工作说明了焦平面传感器处理器在未来的计算成像中可以发挥的有希望的作用。
Computationally removing the motion blur introduced by camera shake or object motion in a captured image remains a challenging task in computational photography. Deblurring methods are often limited by the fixed global exposure time of the image capture process. The post-processing algorithm either must deblur a longer exposure that contains relatively little noise or denoise a short exposure that intentionally removes the opportunity for blur at the cost of increased noise. We present a novel approach of leveraging spatially varying pixel exposures for motion deblurring using next-generation focal-plane sensor-processors along with an end-to-end design of these exposures and a machine learning-based motion-deblurring framework. We demonstrate in simulation and a physical prototype that learned spatially varying pixel exposures (L-SVPE) can successfully deblur scenes while recovering high frequency detail. Our work illustrates the promising role that focal-plane sensor-processors can play in the future of computational imaging.