CogSENet: 基於模糊條件語義路由與顯式頻率融合的盲圖像去模糊

摘要

盲图像去模糊需要在複雜且未知的退化過程中恢復高保真細節與一致結構。現有的盲圖像去模糊方法難以應對真實世界中空間變化的退化情形，且缺乏必要的語義感知能力來可靠區分有效紋理與偽影。為填補此一缺口，我們提出CogSENet，這是一個受鷹類視覺系統啟發的動態語義對齊重建框架。通過模仿鷹的主動掃視運動，我們設計了語義驅動狀態空間模塊（SDSSM），該模塊利用可微路由實現語義感知的令牌重組，從而支持提示條件下的長程依賴建模。為確保紋理與結構的物理可解釋恢復，雙頻融合塊（BFFB）模仿鷹視網膜的功能分化，通過小波變換將特徵分解為高頻與低頻分量。最後，我們從模糊圖像中估計連續模糊場（CBF），並將其與CLIP語義先驗融合，調控最深層的潛在特徵，以模擬焦點適應機制，實現空間非均勻模糊下的自適應恢復。大量實驗表明，CogSENet在更少參數量的情況下，在視覺質量與結構保真度上均優於現有最先進的去模糊方法，同時在去霧、去雨與去噪任務中也表現出色。

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.