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

no code yet • 21 Nov 2022

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

no code yet • 18 Oct 2018

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

no code yet • 18 Oct 2018

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

no code yet • 8 Jun 2018

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

no code yet • 27 May 2018

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.