Towards Differential Handling of Various Blur Regions for Accurate Image Deblurring

• URL: http://arxiv.org/abs/2502.19677v3
• Date: Tue, 04 Mar 2025 02:05:57 GMT
• Title: Towards Differential Handling of Various Blur Regions for Accurate Image Deblurring
• Authors: Hu Gao, Depeng Dang,
• Abstract summary: We propose a differential handling network (DHNet) to perform differential processing for different blur regions.<n>Specifically, we design a Volterra block (VBlock) to integrate the nonlinear characteristics into the deblurring network.<n>To enable the model to adaptively address varying degradation degrees in blurred regions, we devise the degradation degree recognition expert module.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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, or they approximate nonlinear function properties by stacking numerous nonlinear activation functions. 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 integrate the nonlinear characteristics into the deblurring network, avoiding the previous operation of stacking the number of nonlinear activation functions to map complex input-output relationships. 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.

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