An Edge-Preserving Guided Filtering Algorithm Based on Edge Tangent Flow and Side Window

To address the limited edge-preserving capability of traditional guided filtering caused by fixing the filtering window center at the target pixel, an edge-preserving guided filtering algorithm based on edge tangent flow and side window (EWGF) is proposed. Rather than relying on local intensity statistics, the proposed method exploits local geometric structures through the integration of edge-aware weighting, tangent direction estimation, and adaptive side window filtering. Firstly, an edge-aware weighting strategy is introduced to construct a weighted gradient representation, enabling characterization of edge intensity and local structural features. Secondly, based on the weighted gradient field, a structure tensor constrained by local geometric information is established, and edge tangent flow is estimated through eigenvalue decomposition to capture local edge orientations. Furthermore, a tangent-guided one-dimensional side window guided filtering mechanism is developed, in which the filtering direction is adaptively aligned with local edge structures to suppress noise and textures while preserving edge sharpness and structural continuity. Finally, comparative experiments are conducted on the BSD500, Set5, and Set14 datasets, with PSNR and SSIM employed as evaluation metrics. Experimental results demonstrate that, compared with the traditional guided filtering algorithm, the proposed method improves PSNR by an average of 5.69 dB and SSIM by an average of 0.11, validating its performance in structure preservation and noise suppression.