Image deblurring aims to restore a high-quality image from its corresponding blurred. The emergence of CNNs and Transformers has enabled significant progress. However, these methods often face the dilemma between eliminating long-range degradation perturbations and maintaining computational efficiency. While the selective state space model (SSM) shows promise in modeling long-range dependencies with linear complexity, it also encounters challenges such as local pixel forgetting and channel redundancy. To address this issue, we propose an efficient image deblurring network that leverages selective state spaces model to aggregate enriched and accurate features. Specifically, we introduce an aggregate local and global information block (ALGBlock) designed to effectively capture and integrate both local invariant properties and non-local information. The ALGBlock comprises two primary modules: a module for capturing local and global features (CLGF), and a feature aggregation module (FA). The CLGF module is composed of two branches: the global branch captures long-range dependency features via a selective state spaces model, while the local branch employs simplified channel attention to model local connectivity, thereby reducing local pixel forgetting and channel redundancy. In addition, we design a FA module to accentuate the local part by recalibrating the weight during the aggregation of the two branches for restoration. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on widely used benchmarks.

翻译：图像去模糊旨在从模糊图像中恢复出高质量图像。卷积神经网络（CNN）与Transformer的出现推动了该领域的显著进展，然而这些方法常面临消除长距离退化扰动与保持计算效率之间的两难困境。尽管选择性状态空间模型（SSM）在具有线性复杂度的长距离依赖建模方面展现出潜力，但其仍面临局部像素遗忘和通道冗余等挑战。针对此问题，我们提出一种高效图像去模糊网络，利用选择性状态空间模型聚合丰富且精准的特征。具体而言，我们引入聚合局部与全局信息模块（ALGBlock），该模块能够有效捕获并整合局部不变属性与非局部信息。ALGBlock包含两大核心子模块：局部与全局特征捕获模块（CLGF）及特征聚合模块（FA）。CLGF模块由两个分支构成：全局分支通过选择性状态空间模型捕获长距离依赖特征，局部分支则采用简化通道注意力机制建模局部连接性，从而缓解局部像素遗忘与通道冗余问题。此外，我们设计了FA模块，在聚合两个分支特征以进行图像恢复时，通过重新校准权重来强化局部信息。实验结果表明，所提方法在广泛使用的基准数据集上优于现有最优方法。