基于显著性自适应权重图的红外可见光图像融合

Abstract

To solve the problem of insufficient use of source image information by existing fusion methods, a method is proposed using rolling guided filter and anisotropic diffusion to extract the base and detail layers of an image, respectively. These layers were then fused using visual saliency mapping and weight map construction, and a certain weight was added to merge the fused layers into the final image. The proposed method was tested and verified using several scenes from an open dataset. The experimental results show that the final images obtained using the proposed method exhibit better contrast, retain richer texture features at edge details, and maintain a uniform image pixel intensity distribution; furthermore, the visual effects and fusion accuracy of the final images are better than other existing fusion methods. Moreover, significant progress has been made in indicators, such as average gradient, information entropy, and spatial frequency.