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JPEG Artifact Correction

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.

Papers

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Showing 1–18 of 18 papers

TitleStatusHype
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and RestorationCode2
Towards Flexible Blind JPEG Artifacts RemovalCode1
JPEG Artifact Correction using Denoising Diffusion Restoration ModelsCode1
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts RemovalCode1
Hypernetwork-Based Adaptive Image RestorationCode1
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image DenoisingCode1
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip ConnectionsCode0
Residual Dense Network for Image RestorationCode0
MemNet: A Persistent Memory Network for Image RestorationCode0
Multi-level Wavelet-CNN for Image RestorationCode0
Compression Artifacts Reduction by a Deep Convolutional NetworkCode0
Quantization Guided JPEG Artifact CorrectionCode0
Hierarchical Information Flow for Generalized Efficient Image Restoration—0
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction—0
High-Perceptual Quality JPEG Decoding via Posterior Sampling—0
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction—0
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal—0
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images—0
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