Salt-And-Pepper Noise Removal

4 papers with code • 6 benchmarks • 1 datasets

Salt-and-pepper noise is a form of noise sometimes seen on images. It is also known as impulse noise. This noise can be caused by sharp and sudden disturbances in the image signal. It presents itself as sparsely occurring white and black pixels.

( Image credit: NAMF )


Most implemented papers

Noise2Noise: Learning Image Restoration without Clean Data

NVlabs/noise2noise ICML 2018

We apply basic statistical reasoning to signal reconstruction by machine learning -- learning to map corrupted observations to clean signals -- with a simple and powerful conclusion: it is possible to learn to restore images by only looking at corrupted examples, at performance at and sometimes exceeding training using clean data, without explicit image priors or likelihood models of the corruption.

Convolutional Neural Network with Median Layers for Denoising Salt-and-Pepper Contaminations

llmpass/medianDenoise 18 Aug 2019

We propose a deep fully convolutional neural network with a new type of layer, named median layer, to restore images contaminated by the salt-and-pepper (s&p) noise.

NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal

ProfHubert/NAMF 17 Oct 2019

In this paper, a novel algorithm called a non-local adaptive mean filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented.

A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

AliRafiee7/SeConvNet 10 Feb 2023

In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images.