Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS prior. In this paper, we take one step forward by introducing a pixel-level NSS prior, i.e., searching similar pixels across a non-local region. This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance. With the introduced pixel-level NSS prior, we propose an accurate noise level estimation method, and then develop a blind image denoising method based on the lifting Haar transform and Wiener filtering techniques. Experiments on benchmark datasets demonstrate that, the proposed method achieves much better performance than previous non-deep methods, and is still competitive with existing state-of-the-art deep learning based methods on real-world image denoising. The code is publicly available at https://github.com/njusthyk1972/NLH.

中文翻译：

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NLH：一种用于现实世界图像去噪的盲像素级非局部方法


非局部自相似性（NSS）是自然图像用于图像去噪的强大先验。大多数现有的去噪方法都采用类似的补丁，这是补丁级的 NSS 先验。在本文中，我们向前迈出了一步，引入了像素级 NSS 先验，即跨非局部区域搜索相似像素。这是因为在自然图像中找到密切相似的像素比相似的块更可行，这可以用来增强图像去噪性能。通过引入像素级NSS先验，我们提出了一种精确的噪声水平估计方法，然后开发了一种基于提升哈尔变换和维纳滤波技术的盲图像去噪方法。在基准数据集上的实验表明，所提出的方法比以前的非深度方法取得了更好的性能，并且在现实世界图像去噪方面与现有最先进的基于深度学习的方法仍然具有竞争力。该代码可在 https://github.com/njusthyk1972/NLH 上公开获取。