Deep Phase Coded Image Prior
Nimrod Shabtay Eli Schwartz Raja Giryes
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.