DStokes-CGCP: A low-light color polarization image enhancement method combining chroma spectrum and global contour awareness

Abstract

In view of the challenges posed by low light environments, we propose a method for enhancing color polarization images, called DStokes-CGCP. This method employs a dual-branch architecture to effectively leverage the distinct characteristics of different components of the Stokes vector, thereby enhancing the utilization of polarization information. Firstly, the chroma spectrum branch (f_(CS)) extracts features in the spatial and frequency domains based on the low-frequency information of S₀, effectively fusing the information between feature maps of different dimensions, thereby compensating for the loss of spatial domain information and improving image clarity. Secondly, the global contour awareness branch (f_(GCA)) captures the details and edge information of the image through edge and global feature extraction for the high-frequency components of S₁ and S₂, and further enhances the clarity and texture details of S₁ and S₂. Additionally, this study introduces a new dataset, named RCPI. Experimental results show that DStokes-CGCP performs well in improving the quality of low-light images and enhancing the naturalness of the effect.