Image Enhancement Model Based on Multi-scale Dynamic Filtering

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Image Enhancement Model Based on Multi-scale Dynamic Filtering

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[1]YIN Yi,LYU Pei,LI Kaijiang,et al.Image Enhancement Model Based on Multi-scale Dynamic Filtering[J].Journal of Zhengzhou University (Engineering Science),2026,47(3):100-107.[doi:10.13705/j.issn.1671-6833.2025.03.016]

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Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 47 Number of periods: 2026 Issue 3 Page number: 100-107 Column: Public date: 2026-05-27

Title:: Image Enhancement Model Based on Multi-scale Dynamic Filtering

Author(s):: YIN Yi, LYU Pei, LI Kaijiang, ZHENG Haokun, XU Hao, CHEN Mengjie; School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China

Keywords:: image enhancement; low-pass filtering; high-pass filtering; multi-scale fusion; frequency domain transformation

CLC:: TP37:TP391.9

DOI:: 10.13705/j.issn.1671-6833.2025.03.016

Abstract:: To address the issue of collaborative enhancement between global smoothness and local textures in traditional image enhancement techniques, in this study an MDFD image enhancement model based on multi-scale dynamic filtering decomposition was proposed. Initially, learnable low-pass and high-pass filters were utilized to extract the low-frequency and high-frequency image components, respectively. Subsequently, by combining these two frequency-domain image components, the cross low-frequency channel attention fusion module (LFCA) and cross high-frequency spatial attention fusion module (HFSA) were introduced to achieve collaborative enhancement of image global and local features. Finally, a multi-scale fusion strategy was introduced to comprehensively utilize high-frequency and low-frequency information at different scales for feature fusion. The advantage of multi-scale fusion lay in its ability to effectively integrate details and global features at different scales, significantly enhancing the image at multiple levels. Experimental results showed that the MDFD model performed excellently in the validation on the FiveK and PPR10K datasets, with peak signal-to-noise ratio (PSNR) reaching 25.90 and 27.35, structural similarity index (SSIM) being 0.964 and 0.945, and ΔEab being 7.38 and 6.50, respectively. These results indicated that the MDFD model could offer superior image enhancement performance in complex environments and color-rich scenes.

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Last Update: 2026-05-27