Dynamic Degradation for Image Restoration and Fusion
Year: 2021
Type: preprint
Abstract: The deep-learning-based image restoration and fusion methods have achievedremarkable results. However, the existing restoration and fusion methods paid
little research attention to the robustness problem caused by dynamic
degradation. In this paper, we propose a novel dynamic image restoration and
fusion neural network, termed as DDRF-Net, which is capable of solving two
problems, i.e., static restoration and fusion, dynamic degradation. In order to
solve the static fusion problem of existing methods, dynamic convolution is
introduced to learn dynamic restoration and fusion weights. In addition, a
dynamic degradation kernel is proposed to improve the robustness of image
restoration and fusion. Our network framework can effectively combine image
degradation with image fusion tasks, provide more detailed information for
image fusion tasks through image restoration loss, and optimize image
restoration tasks through image fusion loss. Therefore, the stumbling blocks of
deep learning in image fusion, e.g., static fusion weight and specifically
designed network architecture, are greatly mitigated. Extensive experiments
show that our method is more superior compared with the state-of-the-art
methods.
Source: arXiv (Cornell University)
Authors: Aiqing Fang, Xinbo Zhao, Jiaqi Yang, Yanning Zhang
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3157957532