  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.
