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A Comprehensive Benchmark for Single Image Compression Artifacts Reduction
[article]
2019
arXiv
pre-print
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Our survey would give a comprehensive reference source for future research on single image compression artifacts removal and inspire new directions of the related fields. ...
Compression artifacts removal, as a common post-processing technique, aims at alleviating undesirable artifacts such as blockiness, ringing, and banding caused by quantization and approximation in the ...
Deep Dual-Domain (D 3 ) [8] is the first work to introduce
the DCT-domain priors to facilitate JPEG artifacts removal. ...
arXiv:1909.03647v1
fatcat:yujaixpevzeadi7zfh7ap6t7jq
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal
[article]
2018
arXiv
pre-print
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Experiments show that DMCNN sets a new state-of-the-art for the task of JPEG artifact removal. ...
Recently, convolutional neural network (CNN) based methods have shown excellent performance for removing the JPEG artifacts. ...
Specifically for compression artifacts removal, Dong et al. ...
arXiv:1806.03275v2
fatcat:cyiyeuiedfbpbmjufmxhjgp6ce
Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction
[article]
2019
arXiv
pre-print
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2018 pre-print arXiv:1810.08042v1 fatcat:b2dok544dvbrlgibgpu4ls7vu4Other Versions
Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. ...
The implicit dual-domain translation allows the IDCN to handle color images with the discrete cosine transform (DCT)-domain priors. ...
[5] first proposed a deep learning based method (ARCNN) for image compression artifact reduction. ...
arXiv:1810.08042v2
fatcat:bs25zep42vd4dejcargdpa4qjy
D^3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
[article]
2016
arXiv
pre-print
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In this paper, we design a Deep Dual-Domain (D^3) based fast restoration model to remove artifacts of JPEG compressed images. ...
For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. ...
In the paper, we focus on removing artifacts in JPEG compressed images. ...
arXiv:1601.04149v3
fatcat:56v7f6otanbevnc26rrflu3kk4
D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we design a Deep Dual-Domain (D 3 ) based fast restoration model to remove artifacts of JPEG compressed images. ...
For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. ...
In the paper, we focus on removing artifacts in JPEG compressed images. ...
doi:10.1109/cvpr.2016.302
dblp:conf/cvpr/WangLCLYH16
fatcat:3gdoijkpnvc3ndpc5spufu7l4e
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
[article]
2018
arXiv
pre-print
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soft decoding network for JPEG-compressed images, namely DPW-SDNet. ...
JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates. ...
It can be observed that most of the compression artifacts in JPEG images are removed by performing soft decoding on them. ...
arXiv:1805.10558v1
fatcat:sitqnybqmbeexpr36yuoe2yyua
DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
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JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates. ...
soft decoding network for JPEGcompressed images, namely DPW-SDNet. ...
It can be observed that most of the compression artifacts in JPEG images are removed by performing soft decoding on them. ...
doi:10.1109/cvprw.2018.00114
dblp:conf/cvpr/ChenHQXN18
fatcat:rllwqhn2hbdmzckqh5biq7qyxm
Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal
[article]
2020
arXiv
pre-print
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To demonstrate, we focus on the JPEG compression with quality factors, ranging from 1 to 60. ...
The former effectively removes the local artifacts, such as ringing artifacts removal. ...
Such dual-domain presentation can make full use of the compression prior knowledge. Wang et al. ...
arXiv:2009.06912v1
fatcat:5d7h7ec2k5gl3btqk6tyuubd5e
One-two-one networks for compression artifacts reduction in remote sensing
2018
ISPRS journal of photogrammetry and remote sensing (Print)
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2018 pre-print arXiv:1804.00256v1 fatcat:gmyg2l7kgrai7ee6qkeigrnypa
artifacts, especially the blocking effect in compression. ...
Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing. ...
Deep Dual-domain Convolutional neural Network (DDCN) [47] adds DCT-domain prior into the dual networks so that the network is able to learn the difference between the original images and compressed images ...
doi:10.1016/j.isprsjprs.2018.01.003
fatcat:ngv3pss6mvbozl6zzfq2axxrxy
Quantization Guided JPEG Artifact Correction
[article]
2020
arXiv
pre-print
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The JPEG image compression algorithm is the most popular method of image compression because of its ability for large compression ratios. ...
Artifact correction has been studied in the context of deep neural networks for some time, but the current state-of-the-art methods require a different model to be trained for each quality setting, greatly ...
A well known phenomenon of JPEG compression is the removal of high frequency information. ...
arXiv:2004.09320v2
fatcat:p7rpqe3p4bfhpdfzsjyjgnki3a
Towards Flexible Blind JPEG Artifacts Removal
[article]
2021
arXiv
pre-print
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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. ...
removal and details preservation. ...
We leave the study of no-reference IQA for JPEG compression artifacts removal for future works. ...
arXiv:2109.14573v1
fatcat:w2wb7rczvne27ebhzq2lqrhbya
Fast Bayesian JPEG Decompression and Denoising With Tight Frame Priors
2017
IEEE Transactions on Image Processing
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However, for JPEG, such solution is not straightforward, e.g., due to quantization and subsampling of chrominance channels. Derivation of such solution is the main contribution of this paper. ...
Prior knowledge about an image is usually described by the l 1 norm of its sparse domain representation. ...
simultaneously image noise and compression artifacts. ...
doi:10.1109/tip.2016.2627802
pmid:27849529
fatcat:e26ot427kbavxe5cpqyddyk6s4
Learning a Virtual Codec Based on Deep Convolutional Neural Network to Compress Image
[article]
2018
arXiv
pre-print
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After image's valid description, standard image codec such as JPEG is leveraged to further compress image, which leads to image's great distortion and compression artifacts, especially blocking artifacts ...
Meanwhile, an advanced learning algorithm is proposed to train our deep neural networks for compression. ...
In [19] , by the combination of both JPEG prior knowledge and sparse coding expertise, deep dual-domain based restoration is developed for JPEG-compressed images. ...
arXiv:1712.05969v7
fatcat:5so7dj3xzra4rkzcmwn3pdnb6m
Deep Residual Autoencoder for quality independent JPEG restoration
[article]
2019
arXiv
pre-print
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The proposed approach leverages both the learning capacity of deep residual networks and prior knowledge of the JPEG compression pipeline. ...
In this paper we propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) to remove artifacts in JPEG compressed images that is independent from the Quality Factor (QF) used ...
compression artifact removal task. ...
arXiv:1903.06117v1
fatcat:u2ornsmjbbhrlmnvutllwqzk4e
Deep Residual Autoencoder for Blind Universal JPEG Restoration
2020
IEEE Access
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We propose a deep residual autoencoder exploiting Residual-in-Residual Dense Blocks (RRDB) leveraging both the learning capacity of deep residual networks and prior knowledge of the JPEG compression pipeline ...
INDEX TERMS JPEG restoration, deep learning, residual network, autoencoder. ...
ACKNOWLEDGMENT The authors would like to thank the support of NVIDIA Corporation with the donation of the Titan V GPU used for this research. ...
doi:10.1109/access.2020.2984387
fatcat:563n56pjbjc37mav6cy3lnabea
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