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 )
Benchmarks
These leaderboards are used to track progress in Salt-And-Pepper Noise Removal
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BSD
Latest papers with no code
A Two-stage Method for Non-extreme Value Salt-and-Pepper Noise Removal
However, those methods are based on a hypothesis that the value of salt and pepper noise is exactly 0 and 255.
Salt and pepper noise removal method based on stationary Framelet transform with non-convex sparsity regularization
For example, the noise location information is often ignored and the sparsity of the salt and pepper noise is often described by L1 norm, which cannot illustrate the sparse variables clearly.
A Nonlinear Acceleration Method for Iterative Algorithms
Iterative methods have led to better understanding and solving problems such as missing sampling, deconvolution, inverse systems, impulsive and Salt and Pepper noise removal problems.
A Greedy Approach to $\ell_{0,\infty}$ Based Convolutional Sparse Coding
This has the disadvantage that the reconstructed image no longer obeys the sparsity prior used in the processing.
Dual Reweighted Lp-Norm Minimization for Salt-and-pepper Noise Removal
The robust principal component analysis (RPCA), which aims to estimate underlying low-rank and sparse structures from the degraded observation data, has found wide applications in computer vision.
Deep Boosting for Image Denoising
Boosting is a classic algorithm which has been successfully applied to diverse computer vision tasks.
Markov Random Field Model-Based Salt and Pepper Noise Removal
After the formulation of the problem as an inpainting problem, graph cuts with $\alpha$-expansion moves are considered for minimization of the energy functional.
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner.
