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
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration
Very recently, a quaternion-based WNNM approach (QWNNM) has been developed to mitigate this issue, which is capable of representing the color image as a whole in the quaternion domain and preserving the inherent correlation among the three color channels.
A Novel Truncated Norm Regularization Method for Multi-channel Color Image Denoising
However, those methods mostly ignore either the cross-channel difference or the spatial variation of noise, which limits their capacity in real world color image denoising.
Towards Resilient and Secure Smart Grids against PMU Adversarial Attacks: A Deep Learning-Based Robust Data Engineering Approach
In an attempt to provide reliable power distribution, smart grids integrate monitoring, communication, and control technologies for better energy consumption and management.
KBNet: Kernel Basis Network for Image Restoration
zhangyi-3/kbnet
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In this paper, we propose a kernel basis attention (KBA) module, which introduces learnable kernel bases to model representative image patterns for spatial information aggregation.
iiTransformer: A Unified Approach to Exploiting Local and Non-Local Information for Image Restoration
SamsungLabs/iiTransformer
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BMVC 2022
The goal of image restoration is to recover a high-quality image from its degraded input.
Hypernetwork-Based Adaptive Image Restoration
ifryed/HyperRes
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Adaptive image restoration models can restore images with different degradation levels at inference time without the need to retrain the model.
Adversarial Distortion Learning for Medical Image Denoising
mogvision/adl
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The proposed ADL consists of two auto-encoders: a denoiser and a discriminator.
Improving Image Restoration by Revisiting Global Information Aggregation
Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
SwinIR: Image Restoration Using Swin Transformer
In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.
