Abstract: In challenging shooting environments,it is difficult to capture clear and detailed texture information and thermal radiation information using a single infrared or visible image.However,infrared and visible image fusion allows the preservation of thermal radiation information in infrared images and texture details in visible light images.Many existing methods directly generate fused images in the fusion process,ignore the estimation of pixel-level weight contribution of source images,and emphasize the learning between different source images.For this reason,an infrared and visible image fusion based on unsupervised significance mask guidance network is proposed,which uses DenseNet structure to extract comprehensive features from source images.It produces a weight estimation probability to evaluate the contribution of each source image to the fused image.Since infrared and visible images lack ground truth,it is difficult to use supervised learning.UMGN also introduces the significance mask to facilitate the network to focus on learning the thermal radiation information and visible light texture information of infrared images.A weighted fidelity term and gradient loss are also introduced in the training process to prevent gradient degradation.A large number of comparative experiments with other advanced methods prove the superiority and effectiveness of the proposed UMGN method.

Key words: Unsupervised learning, Significance mask, Weight estimation probability, Infrared and visible image fusion