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Image Quality Assessment (IQA) is a challenging task that requires training on massive datasets to achieve accurate predictions.
Image and video quality assessment research at live
Hamid R Sheikh · 2003
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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A statistical evaluation of recent full reference image quality assessment algorithms
Hamid R Sheikh, Muhammad F Sabir, and Alan C Bovik · 2006
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Image information and visual quality
Hamid R Sheikh and Alan C Bovik · 2006
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Most apparent distortion: full-reference image quality assessment and the role of strategy
Eric Cooper Larson and Damon Michael Chandler · 2010
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Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
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Convolutional neural networks for no-reference image quality assessment
Le Kang, Peng Ye, Yi Li, and David Doermann · 2014
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Image database tid2013: Peculiarities, results and perspectives
Nikolay Ponomarenko, Lina Jin, Oleg Ieremeiev, Vladimir Lukin, Karen Egiazarian, Jaakko Astola, Benoit Vozel, Kacem Chehdi, Marco Carli, Federica Battisti, et al · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Fully deep blind image quality predictor
Jongyoo Kim and Sanghoon Lee · 2016
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Perceptual quality prediction on authentically distorted images using a bag of features approach
Deepti Ghadiyaram and Alan C Bovik · 2017
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Deep neural networks for no-reference and full-reference image quality assessment
Sebastian Bosse, Dominique Maniry, Klaus-Robert Müller, Thomas Wiegand, and Wojciech Samek · 2017
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Deep convolutional neural models for picture-quality prediction: Challenges and solutions to data-driven image quality assessment
Jongyoo Kim, Hui Zeng, Deepti Ghadiyaram, Sanghoon Lee, Lei Zhang, and Alan C Bovik · 2017
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Noise2noise: Learning image restoration without clean data
Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Blind predicting similar quality map for image quality assessment
Da Pan, Ping Shi, Ming Hou, Zefeng Ying, Sizhe Fu, and Yuan Zhang · 2018
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Pieapp: Perceptual image-error assessment through pairwise preference
Ekta Prashnani, Hong Cai, Yasamin Mostofi, and Pradeep Sen · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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Blind image quality estimation via distortion aggravation
Xiongkuo Min, Guangtao Zhai, Ke Gu, Yutao Liu, and Xiaokang Yang · 2018
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Blind image quality assessment using a deep bilinear convolutional neural network
Weixia Zhang, Kede Ma, Jia Yan, Dexiang Deng, and Zhou Wang · 2018
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Kadid-10k: A large-scale artificially distorted iqa database
Hanhe Lin, Vlad Hosu, and Dietmar Saupe · 2019
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Perceptual image quality assessment with transformers
Manri Cheon, Sung-Jun Yoon, Byungyeon Kang, and Junwoo Lee · 2021
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Learning conditional knowledge distillation for degraded-reference image quality assessment
Heliang Zheng, Huan Yang, Jianlong Fu, Zheng-Jun Zha, and Jiebo Luo · 2021
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D2c-sr: A divergence to convergence approach for real-world image super-resolution
Youwei Li, Haibin Huang, Lanpeng Jia, Haoqiang Fan, and Shuaicheng Liu · 2022
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Deep constrained least squares for blind image super-resolution
Ziwei Luo, Haibin Huang, Lei Yu, Youwei Li, Haoqiang Fan, and Shuaicheng Liu · 2022
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Ghost-free high dynamic range imaging with context-aware transformer
Zhen Liu, Yinglong Wang, Bing Zeng, and Shuaicheng Liu · 2022
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Training language models to follow instructions with human feedback
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Fast fourier convolution
Lu Chi, Borui Jiang, and Yadong Mu · 2020
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Norm-in-norm loss with faster convergence and better performance for image quality assessment
Dingquan Li, Tingting Jiang, and Ming Jiang · 2020
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Metaiqa: Deep meta-learning for no-reference image quality assessment
Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi · 2020
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Image quality assessment: Unifying structure and texture similarity
Keyan Ding, Kede Ma, Shiqi Wang, and Eero P Simoncelli · 2020
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Blindly assess image quality in the wild guided by a self-adaptive hyper network
Shaolin Su, Qingsen Yan, Yu Zhu, Cheng Zhang, Xin Ge, Jinqiu Sun, and Yanning Zhang · 2020
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Ebsr: Feature enhanced burst super-resolution with deformable alignment
Ziwei Luo, Lei Yu, Xuan Mo, Youwei Li, Lanpeng Jia, Haoqiang Fan, Jian Sun, and Shuaicheng Liu · 2021
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Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Incorporating semi-supervised and positive-unlabeled learning for boosting full reference image quality assessment
Yue Cao, Zhaolin Wan, Dongwei Ren, Zifei Yan, and Wangmeng Zuo · 2022
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Content-variant reference image quality assessment via knowledge distillation
Guanghao Yin, Wei Wang, Zehuan Yuan, Chuchu Han, Wei Ji, Shouqian Sun, and Changhu Wang · 2022
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Dipnet: Efficiency distillation and iterative pruning for image super-resolution
Lei Yu, Xinpeng Li, Youwei Li, Ting Jiang, Qi Wu, Haoqiang Fan, and Shuaicheng Liu · 2023
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Ntire 2023 challenge on efficient super-resolution: Methods and results
Yawei Li, Yulun Zhang, Radu Timofte, Luc Van Gool, Lei Yu, Youwei Li, Xinpeng Li, Ting Jiang, Qi Wu, Mingyan Han, et al · 2023
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Realistic noise synthesis with diffusion models
Qi Wu, Mingyan Han, Ting Jiang, Haoqiang Fan, Bing Zeng, and Shuaicheng Liu · 2023
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Can sam boost video super-resolution?
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Segment anything in medical images
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