# A Convolutional Neural Networks Denoising Approach for Salt and Pepper Noise

arXiv: https://arxiv.org/abs/1807.08176 · HTML: https://mlsanity.com/paper/1807.08176/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [PIQI: Perceptual Image Quality Index based on Ensemble of Gaussian Process Regression](https://mlsanity.com/paper/2305.09214/) (2023)
- [Exploring the Learning Difficulty of Data Theory and Measure](https://mlsanity.com/paper/2205.07427/) (2022)
- [Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization](https://mlsanity.com/paper/2110.09113/) (2021)
- [A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks](https://mlsanity.com/paper/2302.05435/) (2023)

## Abstract (arXiv)

The salt and pepper noise, especially the one with extremely high percentage of impulses, brings a significant challenge to image denoising. In this paper, we propose a non-local switching filter convolutional neural network denoising algorithm, named NLSF-CNN, for salt and pepper noise. As its name suggested, our NLSF-CNN consists of two steps, i.e., a NLSF processing step and a CNN training step. First, we develop a NLSF pre-processing step for noisy images using non-local information. Then, the pre-processed images are divided into patches and used for CNN training, leading to a CNN denoising model for future noisy images. We conduct a number of experiments to evaluate the effectiveness of NLSF-CNN. Experimental results show that NLSF-CNN outperforms the state-of-the-art denoising algorithms with a few training images.