对比NLM与小波去噪在图像增强中的表现，发现其对不同类型噪声的适应性不同。
Quantitative and Qualitative Evaluation of NLM and Wavelet Methods in Image Enhancement
- 比较非局部均值与软小波阈值法在空间与频域的去噪机制。
- 在Set12和SIDD数据集上性能最佳，PSNR、SSIM等指标领先。
- 小波法在频域优化提升SUMMER得分，适合关注频率特征的场景。
本文系统评估了非局部均值（NLM）与达布奇斯软小波阈值法在图像去噪中的表现，针对CURE-OR、CURE-TSD、CURE-TSR、SSID及Set-12数据集进行测试，并采用PSNR、SSIM、CW-SSIM、UNIQUE、MS-UNIQUE、CSV和SUMMER等图像质量评估（IQA）指标进行分析。结果表明，两种方法在Set12和SIDD数据集上表现最优，因其能有效处理一般加性与乘性噪声。但在包含脏镜头、编码错误等复杂失真的CURE数据集上表现受限。进一步对比发现，尽管NLM在视觉质量上更优，但小波阈值法因在频域建模，在SUMMER指标上更具优势。
原文摘要 · Abstract (English)
This paper presents a comprehensive analysis of image denoising techniques, primarily focusing on Non-local Means (NLM) and Daubechies Soft Wavelet Thresholding, and their efficacy across various datasets. These methods are applied to the CURE-OR, CURE-TSD, CURE-TSR, SSID, and Set-12 datasets, followed by an evaluation using Image Quality Assessment (IQA) metrics PSNR, SSIM, CW-SSIM, UNIQUE, MS-UNIQUE, CSV, and SUMMER. The results indicate that NLM and Wavelet Thresholding perform optimally on Set12 and SIDD datasets, attributed to their ability to effectively handle general additive and multiplicative noise masks. However, their performance on CURE datasets is limited due to the presence of complex distortions like Dirty Lens and Codec Error, which these methods are not well-suited to address. Analysis between NLM and Wavelet Thresholding shows that while NLM generally offers superior visual quality, Wavelet Thresholding excels in specific IQA metrics, particularly SUMMER, due to its enhancement in the frequency domain as opposed to NLM's spatial domain approach.
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