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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.

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Datasets

Greatest papers with code

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

13 Aug 2016cszn/DnCNN

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

IMAGE DENOISING IMAGE SUPER-RESOLUTION JPEG ARTIFACT CORRECTION

Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections

29 Jun 2016titu1994/Image-Super-Resolution

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.

IMAGE DENOISING JPEG ARTIFACT CORRECTION SUPER-RESOLUTION

Multi-level Wavelet-CNN for Image Restoration

18 May 2018lpj0/MWCNN

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

IMAGE DENOISING IMAGE SUPER-RESOLUTION JPEG ARTIFACT CORRECTION

Compression Artifacts Reduction by a Deep Convolutional Network

ICCV 2015 volvet/ARCNN

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

DENOISING JPEG ARTIFACT CORRECTION TRANSFER LEARNING

Quantization Guided JPEG Artifact Correction

ECCV 2020 Queuecumber/quantization-guided-ac

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

JPEG ARTIFACT CORRECTION QUANTIZATION