computer-vision-1

Jpeg Compression Artifact Reduction

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Deep Image Prior

Dmitry Ulyanov, A. Vedaldi, V. Lempitsky•Tue Nov 28 2017

It is shown that a randomly-initialized neural network can be used as a handcrafted prior with excellent results in standard inverse problems such as denoising, super-resolution, and inpainting.

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SwinIR: Image Restoration Using Swin Transformer

K. Zhang, L. Gool, Radu Timofte, Jingyun Liang, Guolei Sun, Jie Cao•Sun Aug 22 2021

A strong baseline model SwinIR is proposed for image restoration based on the Swin Transformer that outperforms state-of-the-art methods on different tasks by up to 0.14∼0.45dB, while the total number of parameters can be reduced byUp to 67%.

4127 0

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Accurate Image Restoration with Attention Retractable Transformer

L. Kong, Jinjin Gu, Xin Yuan, Jiale Zhang, Yulun Zhang, Yongbing Zhang•Mon Oct 03 2022

Attention Retractable Transformer (ART) is proposed for image restoration, which presents both dense and sparse attention modules in the network, which greatly enhances representation ability of Transformer while providing retractable attention on the input image.

136 0

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iiTransformer: A Unified Approach to Exploiting Local and Non-local Information for Image Restoration

Hanul Shin, Soo-Min Kang, Y. Song, Tammy Lee•Fri Dec 31 2021

This paper proposes intra-inter Transformer (iiTransformer) by explicitly modelling long-range dependencies at the pixel-and patch-levels since there are benefits to considering both local and non-local feature correlations and provides a boundary artifact-free solution to support images with arbitrary sizes.

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CUR Transformer: A Convolutional Unbiased Regional Transformer for Image Denoising

Ke Yan, Kang Xu, Weixin Li, Xia Wang, Xiaojie Wang, Xiaoyan Hu, Xuan Dong•Mon Oct 10 2022

Experimental results show that the CUR transformer outperforms the state-of-the-art methods significantly on four low-level vision tasks, including real and synthetic image denoising, JPEG compression artifact reduction, and low-light image enhancement.

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