ASFFuse: Infrared and visible image fusion model based on adaptive selection feature maps

Researchers continuously modify deep learning network architecture for improved fusion results. However, little attention is given to the influence of noise feature maps generated during the convolution process on the fusion outcomes. Here, we aim to minimize the influence of noisy feature maps on fusion results and propose a fusion model, the infrared and visible image fusion model based on adaptive selection feature maps (ASFFuse). We propose an adaptive selection feature maps module (ASFM). ASFM measures the amount of information contained in each feature map and filters out feature maps that contain more noise information. Additionally, we introduce a feature enhancement module (FEM) to enrich the fusion image with more source image information. For unsupervised training of the proposed model, we propose a texture loss function based on contrast learning. This loss function preserves the texture information of the image in a better way and makes the fusion image have a better visual effect. Our ASFFuse model has been shown to outperform state-of-the-art models in both quantitative and qualitative evaluations in extensive experiments on the TNO and RoadScene datasets. The code is available at https://github.com/LKZ1584905069/ASFFuse.