Denoising method for color polarization images combining spatial information, color information, and polarization features

Color polarization images generally exhibit a lower signal-to-noise ratio compared to conventional color images, leading to significant quality degradation due to noise sensitivity. To address this issue, this paper proposes a denoising method for color polarization images, referred to as spatial-polarization total variation and polarization-aware joint adaptive sparse and low-rank (SPTV-PASALT). The method processes multiple frames of color polarization filter array (CPFA) images as input; the aim is to enhance the denoising effect of a single frame image through the fusion of multiple frames of information. Initially, the method preserves the basic features of the color polarization images by integrating spatial and polarization information through a SPTV model. Subsequently, the PASALT model effectively combines color and polarization information, thereby reducing color distortion in color polarization images. We evaluate the SPTV-PASALT method using simulated and real CPFA datasets. The experimental results demonstrate that the method significantly suppresses noise while preserving image details and color-polarization information, and its performance in terms of peak signal-to-noise ratio and structural similarity index measure is superior to those of the other existing methods.