Demosaicing of RGBW Color Filter Array Based on Rank Minimization with Colorization Constraint

Abstract

Recently, the white (w) channel has been incorporated in various forms into color filter arrays (CFAs). The advantage of using the W channel is that W pixels have less noise than RGB pixels; therefore, under low-light conditions, pixels with high fidelity can be obtained. However, RGBW CFAs normally suffer from spatial resolution degradation due to a smaller number of color pixels than in RGB CFAs. Therefore, even though the reconstructed colors have higher sensitivity, which results in larger CPSNR values, there are some color aliasing artifacts due to a low resolution. In this paper, we propose a rank minimization-based color interpolation method with a colorization constraint for the RGBW format with a large number of W pixels. The rank minimization can achieve a broad interpolation and preserve the structure in the image, and it thereby eliminates the color artifacts. However, the colors fade from this global process. Therefore, we further incorporate a colorization constraint into the rank minimization process for better reproduction of the colors. Experimental results show that the images can be reconstructed well even from noisy pattern images obtained under low-light conditions.

Low-Rank Tensor Patching Based on Convolutional Sparse Coding for Communication Data Repair

“…Currently, tensor complementation can provide rich applications for data such as image or video restoration [20][21][22], hyperspectral data recovery [23], or nuclear magnetic resonance image data recovery [24][25][26]. In recent years, the research area of low-rank tensors has been extended to numerical analysis, computer vision, machine learning, signal and image processing, etc.…”