论文标题
在空间上学习运动去膨胀的像素暴露
Learning Spatially Varying Pixel Exposures for Motion Deblurring
论文作者
论文摘要
在计算上删除由摄像机摇动或捕获图像中对象运动引入的运动模糊仍然是计算摄影中的一项具有挑战性的任务。脱毛方法通常受图像捕获过程的固定全局曝光时间的限制。后处理算法要么必须消除更长的暴露量，该算法含义相对较少的噪声，要么简短的暴露，这有意以增加噪音的成本来消除模糊的机会。我们提出了一种利用空间变化的像素暴露的新方法，用于使用下一代焦距 - 平面传感器 - 处理器以及这些暴露的端到端设计以及基于机器学习的运动型动作塑造框架。我们在模拟和物理原型中演示了在空间变化的像素暴露（L-SVPE）可以成功地脱布场景的同时，同时恢复高频细节。我们的工作说明了焦点传感器 - 处理器在计算成像的未来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.
