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.

我们提出一种名为 “深度相位编码图像先验” 的新方法，通过仅使用捕获的图像和成像系统的光学信息，从编码相位图像中共同恢复深度地图和全焦图像。

深层相位编码图像先验

Deep Phase Coded Image Prior

Conventional stereo suffers from a fundamental trade-off between imaging
volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of
aperture size on both these variables. Inspired by the extended depth of field
cameras, we propose a novel end-to-end learning-based technique to overcome
this limitation, by introducing a phase mask at the aperture plane of the
cameras in a stereo imaging system. The phase mask creates a depth-dependent
point spread function, allowing us to recover sharp image texture and stereo
correspondence over a significantly extended depth of field (EDOF) than
conventional stereo. The phase mask pattern, the EDOF image reconstruction, and
the stereo disparity estimation are all trained together using an end-to-end
learned deep neural network. We perform theoretical analysis and
characterization of the proposed approach and show a 6x increase in volume that
can be imaged in simulation. We also build an experimental prototype and
validate the approach using real-world results acquired using this prototype
system.

本文提出了一种基于深度神经网络的技术，通过在双目成像系统的光圈平面引入相位掩膜，创造了一个深度相关的点扩展函数，从而克服了传统立体成像中成像体积和信噪比之间的基本折衷，从而获得了较为显著的扩展景深和更清晰的图像纹理，通过理论分析和实验验证，表明该技术可以在成像体积方面提高 6 倍左右。