NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising
This addresses image denoising for applications like photography and medical imaging, but it is incremental as it builds on existing non-local methods.
The paper tackled real-world image denoising by introducing a pixel-level non-local self-similarity prior, which improved performance over previous non-deep methods and remained competitive with state-of-the-art deep learning methods.
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.