Image deblurring is a process of restoring a high quality image from the corresponding blurred image. Significant progress in this field has been made possible by the emergence of various effective deep learning models, including CNNs and Transformers. However, these methods often face the dilemma between eliminating long-range blur degradation perturbations and maintaining computational efficiency, which hinders their practical application. To address this issue, we propose an efficient image deblurring network that leverages selective structured state spaces model to aggregate enriched and accurate features. Specifically, we design an aggregate local and global block (ALGBlock) to capture and fuse both local invariant properties and non-local information. The ALGBlock consists of two blocks: (1) The local block models local connectivity using simplified channel attention. (2) The global block captures long-range dependency features with linear complexity through selective structured state spaces. Nevertheless, we note that the image details are local features of images, we accentuate the local part for restoration by recalibrating the weight when aggregating the two branches for recovery. Experimental results demonstrate that the proposed method outperforms state-of-the-art approaches on widely used benchmarks, highlighting its superior performance.

翻译：图像去模糊是从对应模糊图像中恢复高质量图像的过程。各种有效深度学习模型（包括CNN和Transformer）的出现推动了该领域的重要进展。然而，这些方法常常面临消除长程模糊退化干扰与保持计算效率之间的两难困境，阻碍了其实际应用。为解决这一问题，我们提出一种高效图像去模糊网络，利用选择性结构化状态空间模型聚合丰富且精确的特征。具体而言，我们设计了聚合局部与全局块（ALGBlock）来捕获并融合局部不变特性与非局部信息。ALGBlock包含两个模块：（1）局部模块通过简化通道注意力建模局部连通性；（2）全局模块通过选择性结构化状态空间以线性复杂度捕获长程依赖特征。此外，我们注意到图像细节是图像的局部特征，因此在聚合两个分支进行恢复时，通过重新校准权重来强化局部部分的恢复。实验结果表明，所提方法在广泛使用的基准测试上优于现有最先进方法，突显了其卓越性能。