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

TL;DR

This paper introduces EDD-MAIT, a novel multi-scale adaptive statistical testing approach for edge detection and denoising that enhances detail preservation, noise suppression, and computational efficiency in image processing.

Contribution

It presents a new multi-scale adaptive independence testing method with a gradient-driven window strategy, improving edge detection and denoising performance over existing techniques.

Findings

Outperforms traditional and learning-based methods on benchmark datasets.

Achieves higher F-score, lower MSE, and better PSNR.

Demonstrates robustness against Gaussian noise.

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.