# Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising

arXiv: https://arxiv.org/abs/1803.02112 · HTML: https://mlsanity.com/paper/1803.02112/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

## Built on by (most cited first, 6 of 6)

- [Fast, nonlocal and neural: a lightweight high quality solution to image denoising](https://mlsanity.com/paper/2403.03488/) (2024)
- [VibrantLeaves: A principled parametric image generator for training deep restoration models](https://mlsanity.com/paper/2504.10201/) (2025)
- [Cross-boosting of WNNM Image Denoising method by Directional Wavelet Packets](https://mlsanity.com/paper/2206.04431/) (2022)
- [Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding](https://mlsanity.com/paper/2304.06959/) (2023)
- [Reinforced Diffusion: Learning to Push the Limits of Anisotropic Diffusion for Image Denoising](https://mlsanity.com/paper/2512.24035/) (2025)
- [Learned Nonlocal Feature Matching and Filtering for RAW Image Denoising](https://mlsanity.com/paper/2604.17453/) (2026)

## Abstract (arXiv)

We introduce a paradigm for nonlocal sparsity reinforced deep convolutional neural network denoising. It is a combination of a local multiscale denoising by a convolutional neural network (CNN) based denoiser and a nonlocal denoising based on a nonlocal filter (NLF) exploiting the mutual similarities between groups of patches. CNN models are leveraged with noise levels that progressively decrease at every iteration of our framework, while their output is regularized by a nonlocal prior implicit within the NLF. Unlike complicated neural networks that embed the nonlocality prior within the layers of the network, our framework is modular, it uses standard pre-trained CNNs together with standard nonlocal filters. An instance of the proposed framework, called NN3D, is evaluated over large grayscale image datasets showing state-of-the-art performance.