Multi-scale singular value decomposition polarization image fusion defogging algorithm and experiment

Aiming at the problems that the robust of existing polarization defogging algorithms is poor and image enhancement abilities are limited, an image fusion defogging algorithm based on Multi-scale Singular Value Decomposition (MSVD) is proposed. Firstly, considering the redundancy in polarization measurement information, the least square method is used to improve the accuracy of the polarization information in the traditional defogging algorithm for polarized images; then, with respect to the limitations of that algorithm, a qualitative analysis of the feasibility of image fusion defogging is implemented, and a polarized image fusion defogging algorithm based on multi-scale singular value decomposition is proposed. Finally, a verification experiment under different visibility conditions is designed and quantified. The results show that compared with the classic polarized image defogging algorithm, this algorithm does not require manual parameter adjustment, it has strong adaptability and robustness, and can effectively improve the halos and overexposure of sky areas that occur in the traditional algorithm. The image information entropy and the average gradient can be increased by 18.9% and 38.4% respectively, which effectively improves the quality of visual imaging under complex lighting conditions. The proposed algorithm has great application prospects.