JPEG Artifact Correction
12 papers with code • 26 benchmarks • 5 datasets
Correction of visual artifacts caused by JPEG compression, these artifacts are usually grouped into three types: blocking, blurring, and ringing. They are caused by quantization and removal of high frequency DCT coefficients.
Benchmarks
These leaderboards are used to track progress in JPEG Artifact Correction
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DIV2K
BSDS500
LIVE (Public-Domain Subjective Image Quality Database)
Classic5
ICB
Latest papers
JPEG Artifact Correction using Denoising Diffusion Restoration Models
Diffusion models can be used as learned priors for solving various inverse problems.
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
mv-lab/swin2sr
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Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data.
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.
Towards Flexible Blind JPEG Artifacts Removal
jiaxi-jiang/fbcnn
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ICCV 2021
Training a single deep blind model to handle different quality factors for JPEG image artifacts removal has been attracting considerable attention due to its convenience for practical usage.
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
VDIGPKU/QGCN
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Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance.
Quantization Guided JPEG Artifact Correction
Queuecumber/quantization-guided-ac
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ECCV 2020
The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.
Residual Dense Network for Image Restoration
yulunzhang/RDN
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We fully exploit the hierarchical features from all the convolutional layers.
Multi-level Wavelet-CNN for Image Restoration
With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
MemNet: A Persistent Memory Network for Image Restoration
We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
cszn/DnCNN
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Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
