Phase-coded imaging is a computational imaging method designed to tackle tasks such as passive depth estimation and extended depth of field (EDOF) using depth cues inserted during image capture. Most of the current deep learning-based methods for depth estimation or all-in-focus imaging require a training dataset with high-quality depth maps and an optimal focus point at infinity for all-in-focus images. Such datasets are difficult to create, usually synthetic, and require external graphic programs. We propose a new method named "Deep Phase Coded Image Prior" (DPCIP) for jointly recovering the depth map and all-in-focus image from a coded-phase image using solely the captured image and the optical information of the imaging system. Our approach does not depend on any specific dataset and surpasses prior supervised techniques utilizing the same imaging system. This improvement is achieved through the utilization of a problem formulation based on implicit neural representation (INR) and deep image prior (DIP). Due to our zero-shot method, we overcome the barrier of acquiring accurate ground-truth data of depth maps and all-in-focus images for each new phase-coded system introduced. This allows focusing mainly on developing the imaging system, and not on ground-truth data collection.

翻译：相位编码成像是一种计算成像方法，旨在利用图像捕获过程中插入的深度线索处理被动深度估计和扩展景深等任务。当前大多数基于深度学习的深度估计或全聚焦成像方法需要包含高质量深度图的训练数据集，且全聚焦图像需以无限远为最佳焦点。此类数据集难以创建，通常为合成数据，且需借助外部图形程序。我们提出一种名为"深度相位编码图像先验"（DPCIP）的新方法，仅利用捕获图像与成像系统的光学信息，即可从编码相位图像中联合恢复深度图与全聚焦图像。该方法不依赖任何特定数据集，且超越了使用相同成像系统的现有监督技术。这一改进得益于基于隐式神经表示（INR）和深度图像先验（DIP）的问题建模。由于采用了零样本方法，我们突破了为每个新型相位编码系统获取精确深度图与全聚焦图像真值数据的障碍。这使得研究重心可聚焦于成像系统开发，而非真值数据采集。