FSADFuse: A Novel Fusion Approach to Infrared and Visible Images

Abstract

To address the problems of edge blur and weak detail resolution when fusing infrared and visible images with traditional methods, a novel image fusion approach based on fast super-resolution convolutional neural network and anisotropic diffusion is proposed. The proposed method first employs super-resolution reconstruction based on fast convolutional neural networks to get high-resolution infrared images. Second, the visible and high-resolution infrared images are anisotropically diffused to obtain the corresponding base layers and detail layers. Next, the fusion of the detail layer is performed through Karhunen-Loeve transform, and a saliency adaptive module is constructed to fuse the base layer. Finally, the fusion result is obtained by applying linear reconstruction on the fused detail layer and base layer. Compared with other algorithms, the experimental results show that the proposed algorithm not only has better objective evaluation indexes, but also can better retain the details of the original image. INDEX TERMS Image fusion, infrared and visible images, convolution neural networks, super-resolution reconstruction, multi-scale decomposition.