BM3D-based denoising method for color polarization filter array

Abstract

Color split-focal plane polarization imaging systems are composed of image sensors with a color polarization filter array (CPFA). The noise generated during image acquisition leads to incorrect estimation of the color polarization information. Therefore, it is necessary to denoise CPFA image data. In this study, we propose a CPFA block-matching and 3D filtering (CPFA-BM3D) algorithm for CPFA image data. The algorithm makes full use of the correlation between different polarization channels and different color channels, restricts the grouping of similar 2D image blocks to form 3D blocks, and attenuates Gaussian noise in the transform domain. We evaluate the denoising performance of the proposed algorithm using simulated and real CPFA images. Experimental results show that the proposed method significantly suppresses noise while preserving the image details and polarization information. Its peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) indicators are superior to those of the other existing methods. The mean values of the PSNR and SSIM of the degree of linear polarization (DoLP) color images calculated through CPFA image interpolation can be increased to 200% and 400%, respectively, by denoising with the proposed method.