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

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 S 0, 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 S 1 and S 2, and further enhances the clarity and texture details of S 1 and S 2. 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.