Different Input Resolutions and Arbitrary Output Resolution: A Meta Learning-Based Deep Framework for Infrared and Visible Image Fusion

Infrared and visible image fusion has gained ever-increasing attention in recent years due to its great significance in a variety of vision-based applications. However, existing fusion methods suffer from some limitations in terms of the spatial resolutions of both input source images and output fused image, which prevents their practical usage to a great extent. In this paper, we propose a meta learning-based deep framework for the fusion of infrared and visible images. Unlike most existing methods, the proposed framework can accept the source images of different resolutions and generate the fused image of arbitrary resolution just with a single learned model. In the proposed framework, the features of each source image are first extracted by a convolutional network and upscaled by a meta-upscale module with an arbitrary appropriate factor according to practical requirements. Then, a dual attention mechanism-based feature fusion module is developed to combine features from different source images. Finally, a residual compensation module, which can be iteratively adopted in the proposed framework, is designed to enhance the capability of our method in detail extraction. In addition, the loss function is formulated in a multi-task learning manner via simultaneous fusion and super-resolution, aiming to improve the effect of feature learning. And, a new contrast loss inspired by a perceptual contrast enhancement approach is proposed to further improve the contrast of the fused image. Extensive experiments on widely-used fusion datasets demonstrate the effectiveness and superiority of the proposed method. The code of the proposed method is publicly available at https://github.com/yuliu316316/MetaLearning-Fusion.