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Full-reference image quality assessment (FR-IQA) generally assumes that reference images are of perfect quality.
A law of comparative judgment
Louis L Thurstone · 1927
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Visible differences predictor: An algorithm for the assessment of image fidelity
Scott J Daly · 1992
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Multiscale structural similarity for image quality assessment
Zhou Wang, Eero P Simoncelli, and Alan C Bovik · 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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Predicting visible differences in high dynamic range images: Model and its calibration
Rafal K Mantiuk, Scott J Daly, Karol Myszkowski, and Hans-Peter Seidel · 2005
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Image information and visual quality
Hamid R Sheikh and Alan C Bovik · 2006
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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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FRank: A ranking method with fidelity loss
Ming-Feng Tsai, Tie-Yan Liu, Tao Qin, Hsin-Hsi Chen, and Wei-Ying Ma · 2007
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Mean squared error: Love it or leave it? A new look at signal fidelity measures
Zhou Wang and Alan C Bovik · 2009
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Most apparent distortion: Full-reference image quality assessment and the role of strategy
Eric C Larson and Damon M Chandler · 2010
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HDR-VDP-2: A calibrated visual metric for visibility and quality predictions in all luminance conditions
Rafal K Mantiuk, Kil Joong Kim, Allan G Rempel, and Wolfgang Heidrich · 2011
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Reduced-and no-reference image quality assessment
Zhou Wang and Alan C Bovik · 2011
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FSIM: A feature similarity index for image quality assessment
Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang · 2011
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Making a “completely blind” image quality analyzer
Anish Mittal, Rajiv Soundararajan, and Alan C Bovik · 2012
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VSI: A visual saliency-induced index for perceptual image quality assessment
Lin Zhang, Ying Shen, and Hongyu Li · 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, and Kuo J. C.-C · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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A patch-structure representation method for quality assessment of contrast changed images
Shiqi Wang, Kede Ma, Hojatollah Yeganeh, Zhou Wang, and Weisi Lin · 2015
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Objective quality assessment of interpolated natural images
Hojatollah Yeganeh, Mohammad Rostami, and Zhou Wang · 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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Waterloo exploration database: New challenges for image quality assessment models
Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang · 2016
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Photo-realistic single image super-resolution using a generative adversarial network
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever · 2021
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Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data
Xintao Wang, Liangbin Xie, Chao Dong, and Ying Shan · 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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Attentions help CNNs see better: Attention-based hybrid image quality assessment network
Shanshan Lao, Yuan Gong, Shuwei Shi, Sidi Yang, Tianhe Wu, Jiahao Wang, Weihao Xia, and Yujiu Yang · 2022
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Details or artifacts: A locally discriminative learning approach to realistic image super-resolution
Jie Liang, Hui Zeng, and Lei Zhang · 2022
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Christian Ledig, Lucas Theis, Ferenc Huszár, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, and Wenzhe Shi · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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DIV8K: Diverse 8K resolution image dataset
Shuhang Gu, Andreas Lugmayr, Martin Danelljan, Manuel Fritsche, Julien Lamour, and Radu Timofte · 2019
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A style-based generator architecture for generative adversarial networks
Tero Karras, Samuli Laine, and Timo Aila · 2019
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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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RankSRGAN: Generative adversarial networks with ranker for image super-resolution
Wenlong Zhang, Yihao Liu, Chao Dong, and Yu Qiao · 2019
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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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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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MANIQA: Multi-dimension attention network for no-reference image quality assessment
Sidi Yang, Tianhe Wu, Shuwei Shi, Shanshan Lao, Yuan Gong, Mingdeng Cao, Jiahao Wang, and Yujiu Yang · 2022
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Quality assessment of image super-resolution: Balancing deterministic and statistical fidelity
Wei Zhou and Zhou Wang · 2022
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Human guided ground-truth generation for realistic image super-resolution
Du Chen, Jie Liang, Xindong Zhang, Ming Liu, Hui Zeng, and Lei Zhang · 2023
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LSDIR: A large scale dataset for image restoration
Yawei Li, Kai Zhang, Jingyun Liang, Jiezhang Cao, Ce Liu, Rui Gong, Yulun Zhang, Hao Tang, Yun Liu, Denis Demandolx, Rakesh Ranjan, Radu Timofte, and Luc. Van Gool · 2023
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Improving the stability and efficiency of diffusion models for content consistent super-resolution
Lingchen Sun, Rongyuan Wu, Jie Liang, Zhengqiang Zhang, Hongwei Yong, and Lei Zhang · 2023
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Pixel-level and semantic-level adjustable super-resolution: A dual-LoRA approach
Lingchen Sun, Rongyuan Wu, Zhiyuan Ma, Shuaizheng Liu, Qiaosi Yi, and Lei Zhang · 2024
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Pixel-aware stable diffusion for realistic image super-resolution and personalized stylization
Tao Yang, Rongyuan Wu, Peiran Ren, Xuansong Xie, and Lei Zhang · 2024
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Scaling up to excellence: Practicing model scaling for photo-realistic image restoration in the wild
Fanghua Yu, Jinjin Gu, Zheyuan Li, Jinfan Hu, Xiangtao Kong, Xintao Wang, Jingwen He, Yu Qiao, and Chao Dong · 2024
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Teaching large language models to regress accurate image quality scores using score distribution
Zhiyuan You, Xin Cai, Jinjin Gu, Tianfan Xue, and Chao Dong · 2025
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