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

arXiv: https://arxiv.org/abs/1906.06834 · HTML: https://mlsanity.com/paper/1906.06834/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 15 of 15)

- [UMCFuse: A Unified Multiple Complex Scenes Infrared and Visible Image Fusion Framework](https://mlsanity.com/paper/2402.02096/) (2024)
- [A Comparison of Image Denoising Methods](https://mlsanity.com/paper/2304.08990/) (2023)
- [Image Denoising Using Green Channel Prior](https://mlsanity.com/paper/2408.05923/) (2024)
- [Joint Super-Resolution and Inverse Tone-Mapping: A Feature Decomposition Aggregation Network and A New Benchmark](https://mlsanity.com/paper/2207.03367/) (2022)
- [Guided Colorization Using Mono-Color Image Pairs](https://mlsanity.com/paper/2108.07471/) (2021)
- [Self-Verification in Image Denoising](https://mlsanity.com/paper/2111.00666/) (2021)
- [Reinforcement Learning of Self Enhancing Camera Image and Signal Processing](https://mlsanity.com/paper/2111.07499/) (2021)
- [FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation](https://mlsanity.com/paper/2203.03624/) (2022)
- [Color Image Denoising Using The Green Channel Prior](https://mlsanity.com/paper/2402.08235/) (2024)
- [Deep Contrastive Patch-Based Subspace Learning for Camera Image Signal Processing](https://mlsanity.com/paper/2104.00253/) (2021)
- [Speckles-Training-Based Denoising Convolutional Neural Network Ghost Imaging](https://mlsanity.com/paper/2104.02873/) (2021)
- [NLHD: A Pixel-Level Non-Local Retinex Model for Low-Light Image Enhancement](https://mlsanity.com/paper/2106.06971/) (2021)
- [An Improved U-Net Model for Offline handwriting signature denoising](https://mlsanity.com/paper/2507.00365/) (2025)
- [Efficient Image Denoising Using Global and Local Circulant Representation](https://mlsanity.com/paper/2508.10307/) (2025)
- [Image Denoising Using Global and Local Circulant Representation](https://mlsanity.com/paper/2512.23569/) (2025)

## Abstract (arXiv)

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.