Browse State-of-the-Art › Salt-And-Pepper Noise Removal

Salt-And-Pepper Noise Removal

5 papers with code · 6 benchmarks · 1 dataset archive 2025-07-28

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 )

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

6 leaderboard tables shown for this task, 6 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Dataset	Best model (first row in archive order)	Paper	Code	Syntology	Compare
BSD300 Noise Level 30% (3 rows)	RTF-Net	Residual Transformer Fusion Network for Salt and Pepper Image Denoising	—	—	Compare
BSD300 Noise Level 50% (3 rows)	RTF-Net	Residual Transformer Fusion Network for Salt and Pepper Image Denoising	—	—	Compare
BSD300 Noise Level 70% (3 rows)	RTF-Net	Residual Transformer Fusion Network for Salt and Pepper Image Denoising	—	—	Compare
Kodak24 Noise Level 30% (3 rows)	CNN (Median Layers)	Convolutional Neural Network with Median Layers for Denoising...	code	—	Compare
Kodak24 Noise Level 50% (3 rows)	CNN (Median Layers)	Convolutional Neural Network with Median Layers for Denoising...	code	—	Compare
Kodak24 Noise Level 70% (3 rows)	CNN (Median Layers)	Convolutional Neural Network with Median Layers for Denoising...	code	—	Compare

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

BSD

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Denoising

Most implemented papers archive 2025-07-28

5 shown of 5 papers with code (13 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

Noise2Noise: Learning Image Restoration without Clean Data

12 Mar 2018 21 repositories listed Syntology ran 1 of 7 samples · 6 unverified · 1 pointer-only (licence)

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…
Adaptive Weight Modified Riesz Mean Filter For High-Density Salt and Pepper Noise Removal

25 Apr 2025 1 repository listed

This paper introduces a novel filter, the Adaptive Weight Modified Riesz Mean Filter (AWMRmF), designed for the effective removal of high-density salt and pepper noise (SPN).
A deep convolutional neural network for salt-and-pepper noise removal using selective convolutional blocks

10 Feb 2023 1 repository listed

In this paper, we proposed a deep CNN model, namely SeConvNet, to suppress SAP noise in gray-scale and color images.
NAMF: A Non-local Adaptive Mean Filter for Salt-and-Pepper Noise Removal

17 Oct 2019 1 repository listed

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

18 Aug 2019 1 repository listed

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.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.

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