Polarization fusion algorithm based on NSCT decomposition and improved dual-channel PCNN model

Polarization imaging is capable of effectively showcasing the polarization properties of objects while also mitigating the impact of strong light and enhancing the visibility of weak light. As a result, it compensates for the limitations of intensity imaging in low-light environments. To fully utilize the advantages of intensity and polarization images, this paper proposes a method that combines NSCT decomposition and an improved dual-channel PCNN model. The method enhances the traditional PCNN model by extending the input to dual channels, employing adaptively calculated parameters, and applying adaptive linking weights to the high- and low-frequency images obtained from NSCT decomposition. The highfrequency image utilizes the MSMG operator, while the low-frequency image employs a weight map controlled by multiple parameters. This method is utilized for fusion processing of intensity and polarization images.By conducting comparative experiments with six commonly used algorithms, the results demonstrate the superior performance of this method in terms of preserving texture details, providing high-quality images, achieving information-rich fusion, generating fused images with rich grayscale levels, and maintaining structural similarity. In summary, the proposed method exhibits significant advantages in fusing intensity and polarization images.