Joint constraints of guided filtering based confidence and nonlocal sparse tensor for color polarization super-resolution imaging

Abstract

This paper introduces a camera-array-based super-resolution color polarization imaging system designed to simultaneously capture color and polarization information of a scene in a single shot. Existing snapshot color polarization imaging has a complex structure and limited generalizability, which are overcome by the proposed system. In addition, a novel reconstruction algorithm is designed to exploit the complementarity and correlation between the twelve channels in acquired color polarization images for simultaneous super-resolution (SR) imaging and denoising. We propose a confidence-guided SR reconstruction algorithm based on guided filtering to enhance the constraint capability of the observed data. Additionally, by introducing adaptive parameters, we effectively balance the data fidelity constraint and the regularization constraint of nonlocal sparse tensor. Simulations were conducted to compare the proposed system with a color polarization camera. The results show that color polarization images generated by the proposed system and algorithm outperform those obtained from the color polarization camera and the state-of-the-art color polarization demosaicking algorithms. Moreover, the proposed algorithm also outperforms state-of-the-art SR algorithms based on deep learning. To evaluate the applicability of the proposed imaging system and reconstruction algorithm in practice, a prototype was constructed for color polarization image acquisition. Compared with conventional acquisition, the proposed solution demonstrates a significant improvement in the reconstructed color polarization images.