A Sparse Representation Based Joint Demosaicing Method for Single-Chip Polarized Color Sensor

TL;DR

This paper introduces a novel sparse representation-based joint demosaicing method for single-chip polarized color sensors, effectively reconstructing full RGB and polarization information from mosaic images, outperforming traditional techniques.

Contribution

The paper presents a new joint demosaicing model utilizing sparse representation and separate dictionaries for color and polarization, tailored for polarized color sensors.

Findings

Accurately recovers full RGB and polarization angles from mosaic images.

Performs well on both synthetic and real captured data.

Outperforms traditional interpolation methods.

Abstract

The emergence of the single-chip polarized color sensor now allows for simultaneously capturing chromatic and polarimetric information of the scene on a monochromatic image plane. However, unlike the usual camera with an embedded demosaicing method, the latest polarized color camera is not delivered with an in-built demosaicing tool. For demosaicing, the users have to down-sample the captured images or to use traditional interpolation techniques. Neither of them can perform well since the polarization and color are interdependent. Therefore, joint chromatic and polarimetric demosaicing is the key to obtaining high-quality polarized color images. In this paper, we propose a joint chromatic and polarimetric demosaicing model to address this challenging problem. Instead of mechanically demosaicing for the multi-channel polarized color image, we further present a sparse representation-based optimization strategy that utilizes chromatic information and polarimetric information to jointly optimize the model. To avoid the interaction between color and polarization during demosaicing, we separately construct the corresponding dictionaries. We also build an optical data acquisition system to collect a dataset, which contains various sources of polarization, such as illumination, reflectance and birefringence. Results of both qualitative and quantitative experiments have shown that our method is capable of faithfully recovering full RGB information of four polarization angles for each pixel from a single mosaic input image. Moreover, the proposed method can perform well not only on the synthetic data but the real captured data.