# NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising

## TL;DR

This paper introduces a pixel-level non-local self similarity prior for real-world image denoising, leading to a blind denoising method that outperforms previous non-deep methods and rivals deep learning approaches.

## Contribution

It proposes a novel pixel-level NSS prior and a blind denoising method using lifting Haar transform and Wiener filtering, advancing beyond patch-level NSS methods.

## Key findings

- Outperforms previous non-deep denoising methods on benchmarks.
- Achieves competitive results with state-of-the-art deep learning methods.
- Provides a publicly available code implementation.

## Abstract

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.