Towards Differential Handling of Various Blur Regions for Accurate Image Deblurring

2025-02-27

AI Summary

Image deblurring faces two major challenges: spatially non-uniform blur degradation and the difficulty of accurately modeling its highly nonlinear characteristics. To address these, we propose the Differentiated Handling Network (DHNet). First, we design a novel Volterra Block (VBlock) that explicitly models pixel-wise nonlinear degradation relationships. Second, we introduce a Degradation Degree Recognition Expert (DDRE) module, which dynamically assigns spatial weights based on local blur intensity and scale to enable adaptive spatial modeling. Third, DHNet integrates prior-guided spatial degradation estimation within an end-to-end differentiable architecture. Extensive experiments on both synthetic and real-world datasets demonstrate that DHNet significantly outperforms state-of-the-art methods, achieving superior PSNR and SSIM scores while exhibiting enhanced generalization and robustness.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Non-convex Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Accurate image deblurring with differential handling
• Integration of nonlinear characteristics via Volterra block
• Adaptive degradation recognition using expert module

Innovation

• Differential handling network for blur
• Volterra block for nonlinear integration
• Degradation degree recognition expert module