Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing

• URL: http://arxiv.org/abs/2505.01032v1
• Date: Fri, 02 May 2025 06:09:32 GMT
• Title: Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing
• Authors: Ruyu Yan, Da-Qing Zhang,
• Abstract summary: We propose a novel Multi-scale Adaptive Independence Testing-based Edge Detection and Denoising (EDD-MAIT)<n>EDD-MAIT integrates a channel attention mechanism with independence testing.<n>It achieves better robustness, accuracy, and efficiency, with improvements in F-score, MSE, PSNR, and reduced runtime.
• Score: 2.8724598079549715
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Edge detection is crucial in image processing, but existing methods often produce overly detailed edge maps, affecting clarity. Fixed-window statistical testing faces issues like scale mismatch and computational redundancy. To address these, we propose a novel Multi-scale Adaptive Independence Testing-based Edge Detection and Denoising (EDD-MAIT), a Multi-scale Adaptive Statistical Testing-based edge detection and denoising method that integrates a channel attention mechanism with independence testing. A gradient-driven adaptive window strategy adjusts window sizes dynamically, improving detail preservation and noise suppression. EDD-MAIT achieves better robustness, accuracy, and efficiency, outperforming traditional and learning-based methods on BSDS500 and BIPED datasets, with improvements in F-score, MSE, PSNR, and reduced runtime. It also shows robustness against Gaussian noise, generating accurate and clean edge maps in noisy environments.

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