# JPEG Artifact Correction

9 papers with code • 23 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.

# Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

29 Jun 2016

In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.

17

# Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

13 Aug 2016

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.

14

# Multi-level Wavelet-CNN for Image Restoration

18 May 2018

With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.

5

# Compression Artifacts Reduction by a Deep Convolutional Network

Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring.

3

# 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.

2

# Residual Dense Network for Image Restoration

25 Dec 2018

We fully exploit the hierarchical features from all the convolutional layers.

2

# Towards Flexible Blind JPEG Artifacts Removal

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.

2

# Quantization Guided JPEG Artifact Correction

The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios.

1

# Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal

15 Sep 2020

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.

1