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

NLH: A Blind Pixel-Level Non-Local Method for Real-World Image Denoising