Abstract
The purpose of infrared and visible image fusion is to obtain an image that includes both infrared target and visible information. However, among the existing infrared and visible image fusion methods, some of them give priority to the fusion effect, often with complex design, ignoring the influence of attention mechanisms on deep features, resulting in the lack of visible light texture information in the fusion image. To solve these problems, an infrared and visible image fusion method based on dense gradient attention residuals is proposed in this article. Firstly, squeeze-and-excitation networks are integrated into the gradient convolutional dense block, and a new gradient attention residual dense block is designed to enhance the ability of the network to extract important information. In order to retain more original image information, the feature gradient attention module is introduced to enhance the ability of detail information retention. In the fusion layer, an adaptive weighted energy attention network based on an energy fusion strategy is used to further preserve the infrared and visible details. Through the experimental comparison on the TNO dataset, our method has excellent performance on several evaluation indicators. Specifically, in the indexes of average gradient (AG), information entropy (EN), spatial frequency (SF), mutual information (MI) and standard deviation (SD), our method reached 6.90, 7.46, 17.30, 2.62 and 54.99, respectively, which increased by 37.31%, 6.55%, 32.01%, 8.16%, and 10.01% compared with the other five commonly used methods. These results demonstrate the effectiveness and superiority of our method.