  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.
