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