自适应窗口统计检验法提升图像去噪边缘保持能力
Edge-preserving Image Denoising via Multi-scale Adaptive Statistical Independence Testing
- 动态调整窗口大小，结合通道注意力与独立性检验
- 在BSDS500和BIPED数据集上F-score、PSNR更优，运行更快
- 适合对边缘细节敏感的图像去噪场景
边缘检测在图像处理中至关重要，但现有方法常生成过度细致的边缘图，影响清晰度。固定窗口的统计检验存在尺度不匹配与计算冗余问题。为此，我们提出基于多尺度自适应独立性检验的边缘检测与去噪方法（EDD-MAIT），融合通道注意力机制与独立性检验。通过梯度驱动的自适应窗口策略动态调整窗口尺寸，提升细节保留与噪声抑制能力。EDD-MAIT在BSDS500和BIPED数据集上优于传统及学习型方法，F-score、MSE、PSNR均有提升，运行时间减少。对高斯噪声具有鲁棒性，在噪声环境下仍能生成准确清晰的边缘图。
原文摘要 · Abstract (English)
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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