A Sparse Representation Based Joint Demosaicing Method for Single-Chip Polarized Color Sensor
This addresses a specific challenge in computational imaging for users of polarized color cameras, offering a solution to improve image quality, but it is incremental as it builds on existing demosaicing and sparse representation techniques.
The paper tackles the problem of joint chromatic and polarimetric demosaicing for single-chip polarized color sensors, which lack built-in tools, by proposing a sparse representation-based optimization model that recovers full RGB information for four polarization angles from a single mosaic image, with results validated on both synthetic and real captured data.
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.