Salt-And-Pepper Noise Removal Edit Task
Computer Vision • Denoising
About
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 )
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
|
||||||
|
||||||
|
||||||
|
||||||
|
||||||
|
Datasets
Greatest papers with code
Noise2Noise: Learning Image Restoration without Clean Data
ICML 2018
• NVlabs/noise2noise
• 
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
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
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.