Towards Differential Handling of Various Blur Regions for Accurate Image Deblurring

Hu Gao, XiaoNing Lei, Xichen Xu, Depeng Dang, Lizhuang Ma

Keywords: Image restoration, Image deblurring, Differential Handling

Abstract: Image deblurring aims to restore high-quality images by removing undesired degradation. Although existing methods have yielded promising results, they either overlook the varying degrees of degradation across different regions of the blurred image. In this paper, we propose a differential handling network (DHNet) to perform differential processing for different blur regions. Specifically, we design a Volterra block (VBlock) to incorporate nonlinear characteristics into the deblurring network, enabling it to map complex input-output relationships without relying on nonlinear activation functions. To enable the model to adaptively address varying degradation degrees in blurred regions, we devise the degradation degree recognition expert module (DDRE). This module initially incorporates prior knowledge from a well-trained model to estimate spatially variable blur information. Consequently, the router can map the learned degradation representation and allocate weights to experts according to both the degree of degradation and the size of the regions. Comprehensive experimental results show that DHNet effectively surpasses state-of-the-art (SOTA) methods on both synthetic and real-world datasets.

Primary Area: applications to computer vision, audio, language, and other modalities

Submission Number: 7251