CogSENet：基于模糊条件语义路由和显式频率融合的盲图像去模糊

摘要

盲图像去模糊要求从复杂、未知的退化中恢复高保真细节和连贯结构。当前盲图像去模糊方法难以应对真实世界中空间变化的退化，且缺乏必要的语义感知能力来可靠区分有效纹理与伪影。为弥补这一差距，我们提出CogSENet，一种受鹰视觉系统启发的动态、语义对齐的重构框架。通过模仿鹰的主动扫视扫描，我们设计了语义驱动状态空间模块（SDSSM），利用可微路由实现语义感知的令牌重分组，从而支持条件驱动的长程依赖建模。为确保纹理和结构的物理可解释恢复，双频融合块（BFFB）通过小波变换将特征分解为高频和低频，模拟了鹰视网膜的功能分化。最后，我们从模糊图像中估计连续模糊场（CBF），并将其与CLIP语义先验融合，以调节最深层的潜在特征，模拟焦点适应，从而实现空间非均匀模糊下的自适应恢复。大量实验表明，CogSENet在视觉质量和结构保真度上均优于最先进的去模糊方法，且参数量更少；同时在去雾、去雨和去噪任务上也表现出色。

English

Blind image deblurring demands the recovery of high-fidelity details and coherent structures from complex, unknown degradations. Current blind image deblurring methods struggle with real-world, spatially varying degradations, and lack the semantic awareness necessary to reliably differentiate valid textures from artifacts. To bridge this gap, we propose CogSENet, a dynamic, semantic-aligned reconstruction framework inspired by the eagle's visual system. By mimicking the eagle's active saccadic scanning, we devise a Semantic-Driven State Space Module (SDSSM) with semantic-aware token regrouping via differentiable routing, enabling prompt-conditioned long-range dependency modeling. To ensure physically interpretable recovery of textures and structures, a BiFreqFusionBlock (BFFB) mirrors functional differentiation of the eagle's retina by decomposing features into high and low frequencies using wavelet transforms. Finally, we estimate a continuous Blur Field (CBF) from blur image and fuse it with CLIP semantic priors to modulate the deepest latent features, emulating focal adaptation and enabling adaptive restoration under spatially non-uniform blur. Extensive experiments demonstrate that CogSENetoutperforms state-of-the-art deblurring methods in both visual quality and structural fidelity with fewer parameters, while also performing favorably on dehazing, deraining, and denoising tasks.