Color Image Denoising
27 papers with code • 61 benchmarks • 8 datasets
Benchmarks
These leaderboards are used to track progress in Color Image Denoising
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Datasets
BSD
Urban100
CBSD68
McMaster
Cell
RENOIR
Fingerprint inpainting and denoising
Latest papers with no code
Color Image Denoising Using The Green Channel Prior
In this paper, we propose a simple and effective one step GCP-based image denoising (GCP-ID) method, which aims to exploit the GCP for denoising in the sRGB space by integrating it into the classic nonlocal transform domain denoising framework.
A Dive into SAM Prior in Image Restoration
This motivates the investigation and incorporation of prior knowledge in order to effectively constrain the solution space and enhance the quality of the restored images.
SwinIA: Self-Supervised Blind-Spot Image Denoising with Zero Convolutions
The essence of self-supervised image denoising is to restore the signal from the noisy image alone.
Polarized Color Image Denoising
Single-chip polarized color photography provides both visual textures and object surface information in one snapshot.
Multi-channel Nuclear Norm Minus Frobenius Norm Minimization for Color Image Denoising
Specifically, based on the block-matching, the color image is decomposed into overlapping RGB patches.
Polarized Color Image Denoising using Pocoformer
Polarized color photography provides both visual textures and object surficial information in one single snapshot.
Quaternion higher-order singular value decomposition and its applications in color image processing
In more recent years, the quaternion has proven to be a very suitable tool for color pixel representation as it can well preserve cross-channel correlation of color channels.
Frequency-Weighted Robust Tensor Principal Component Analysis
The newly obtained frequency-weighted RTPCA can be solved by alternating direction method of multipliers, and it is the first time that frequency analysis is taken in tensor principal component analysis.
KRNET: Image Denoising with Kernel Regulation Network
In this paper, we optimize CNN regularization capability by developing a kernel regulation module.
Universal Denoising Networks : A Novel CNN Architecture for Image Denoising
As opposed to most of the existing deep network approaches, which require the training of a specific model for each considered noise level, the proposed models are able to handle a wide range of noise levels using a single set of learned parameters, while they are very robust when the noise degrading the latent image does not match the statistics of the noise used during training.
