CogSENet: Blind Image Deblurring with Blur-Conditioned Semantic Routing and Explicit Frequency Fusion

Blind image deblurring demands the recovery of high-fidelitydetails and coherent structures from complex, unknown degradations.Current blind image deblurring methods struggle with real-world, spa-tially varying degradations, and lack the semantic awareness necessary toreliably differentiate valid textures from artifacts. To bridge this gap, wepropose CogSENet, a dynamic, semantic-aligned reconstruction frame-work inspired by the eagle’s visual system. By mimicking the eagle’sactive saccadic scanning, we devise a Semantic-Driven State Space Mod-ule (SDSSM) with semantic-aware token regrouping via differentiablerouting, enabling prompt-conditioned long-range dependency modeling.To ensure physically interpretable recovery of textures and structures,a BiFreqFusionBlock (BFFB) mirrors functional differentiation of theeagle’s retina by decomposing features into high and low frequenciesusing wavelet transforms. Finally, we estimate a continuous Blur Field(CBF) from blur image and fuse it with CLIP semantic priors to modu-late the deepest latent features, emulating focal adaptation and enablingadaptive restoration under spatially non-uniform blur. Extensive exper-iments demonstrate that CogSENet outperforms state-of-the-art deblur-ring methods in both visual quality and structural fidelity with fewerparameters, while also performing favorably on dehazing, deraining, anddenoising tasks.