Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing
This addresses edge detection and denoising for image processing applications, offering incremental improvements over existing methods.
The paper tackles the problem of edge detection and denoising in images, where existing methods produce overly detailed edge maps and face scale mismatch issues, by proposing EDD-MAIT, which integrates multi-scale adaptive statistical testing with a channel attention mechanism, resulting in improved F-score, MSE, PSNR, and reduced runtime on BSDS500 and BIPED datasets.
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.