MDbFusion++: A Visible and Infrared Image Fusion Framework Capable for Motion Deblurring.

Abstract

Existing image fusion methods focus on containing more complementary information, but source images always suffer from motion blur owing to object motion, which results in distorted details in fused images and further deteriorates performance on high-level tasks. This paper proposes a novel visible and infrared image fusion framework capable for motion deblurring (MDbFusion++), which can simultaneously perform image fusion and deblurring within a mutually reinforcing framework. MDbFusion++ employs a coarse-to-fine image restoration strategy and comprises two key components: a coarse deblurring part (CDP) and a fine deblurring and fusion part (FDFP). Firstly, CDP transfers multi-modal images into features corresponding to spatial locations and creatively leverages infrared features to coarsely compensate motion blurred visible ones through adaptive weights module (AWM). Subsequently, FDFP further restores fine visible features and achieves multi-modal images fusion in spatial and frequency domains with the help of multi-domain enhancement module (MEM). The deblurred visible features provide clear information to improve fusion results, and the improved fused images, in turn, provide gradient feedback to further improve deblurring effects. We evaluate our network in terms of both image deblurring and fusion, and extensive comparative experiments demonstrate the superior performance and distinct advantages of MDbFusion++.