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.
Latest papers with no code
High-Perceptual Quality JPEG Decoding via Posterior Sampling
JPEG is arguably the most popular image coding format, achieving high compression ratios via lossy quantization that may create visual artifacts degradation.
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
A dense block was introduced to improve the performance of extractor in DRU.
S-Net: A Scalable Convolutional Neural Network for JPEG Compression Artifact Reduction
Recent studies have used deep residual convolutional neural networks (CNNs) for JPEG compression artifact reduction.
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal
JPEG is one of the most commonly used standards among lossy image compression methods.
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction.