Research on Polarized Image Fusion Algorithm Based on Image Enhancement

This paper proposes a polarization image fusion algorithm based on CNN, Transformer, and attention mechanisms to address the issue of inaccurate scene representation in visible light intensity images under certain conditions. The algorithm network consists of an encoder, fusion module, and decoder. Constructing residual dense blocks in the encoder helps preserve more feature information, thereby enhancing the network’s stability. Within the fusion module, channel attention mechanisms are integrated into the intensity feature map extraction network to enhance the responsiveness of important feature channels. Simultaneously, spatial attention mechanisms are embedded into the polarization feature map extraction network to capture critical features across different spatial positions. The use of Sobel operators to extract gradient information from shallow feature maps effectively enhances the network’s capability to extract detailed features. These optimization measures collectively improve the utilization of feature maps, thereby enhancing the overall performance of the network in image processing tasks. Experimental results demonstrate that the fused images produced by this algorithm not only achieve optimal values across multiple objective evaluation metrics but also exhibit superior visual quality that aligns better with human visual perception.