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To support the application scenarios where high-resolution (HR) images are urgently needed, various single image super-resolution (SISR) algorithms are developed.
“Super-resolution image reconstruction: a technical overview,”
Sung Cheol Park, Min Kyu Park, and Moon Gi Kang, · 2003
Earlier work this paper cites.
“Image quality assessment: from error visibility to structural similarity,”
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli, · 2004
Earlier work this paper cites.
“Semi-blind image restoration via mumford-shah regularization,”
L. Bar, N. Sochen, and N. Kiryati, · 2006
Earlier work this paper cites.
“Most apparent distortion: full-reference image quality assessment and the role of strategy,”
Eric Cooper Larson and Damon Michael Chandler, · 2010
Earlier work this paper cites.
“Nonlocal mumford-shah regularizers for color image restoration,”
Miyoun Jung, Xavier Bresson, Tony F. Chan, and Luminita A. Vese, · 2011
Earlier work this paper cites.
“Fsim: A feature similarity index for image quality assessment,”
Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang, · 2011
Earlier work this paper cites.
“No-reference image quality assessment in the spatial domain,”
Anish Mittal, Anush Krishna Moorthy, and Alan Conrad Bovik, · 2012
Earlier work this paper cites.
“Making a “completely blind” image quality analyzer,”
A. Mittal, R. Soundararajan, and A. C. Bovik, · 2013
Earlier work this paper cites.
“Gradient magnitude similarity deviation: A highly efficient perceptual image quality index,”
Wufeng Xue, Lei Zhang, Xuanqin Mou, and Alan C. Bovik, · 2014
Cited alongside, same era.
“Very deep convolutional networks for large-scale image recognition,” 2015
Karen Simonyan and Andrew Zisserman, · 2015
Cited alongside, same era.
“Deep residual learning for image recognition,”
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun, · 2016
Cited alongside, same era.
“Learning a no-reference quality metric for single-image super-resolution,”
Chao Ma, Chih-Yuan Yang, Xiaokang Yang, and Ming-Hsuan Yang, · 2017
Cited alongside, same era.
“Convolutional neural network for blind quality evaluator of image super-resolution,”
Yuming Fang and Chi Zhang, · 2017
Cited alongside, same era.
“Mobilenets: Efficient convolutional neural networks for mobile vision applications,” 2017
“Dynamic backlight scaling considering ambient luminance for mobile videos on lcd displays,”
W. Sun, X. Min, G. Zhai, K. Gu, S. Ma, and X. Yang, · 2020
Later among the works it cites.
“Blind image quality assessment for super resolution via optimal feature selection,”
Juan Beron, Hernan Dario Benitez-Restrepo, and Alan C. Bovik, · 2020
Later among the works it cites.
“Blind quality assessment for image superresolution using deep two-stream convolutional networks,”
Wei Zhou, Qiuping Jiang, Yuwang Wang, Zhibo Chen, and Weiping Li, · 2020
Later among the works it cites.
“Image super-resolution quality assessment: Structural fidelity versus statistical naturalness,”
Wei Zhou, Zhou Wang, and Zhibo Chen, · 2021
Later among the works it cites.
“A no-reference evaluation metric for low-light image enhancement,”
Zicheng Zhang, Wei Sun, Xiongkuo Min, Wenhan Zhu, Tao Wang, Wei Lu, and Guangtao Zhai, · 2021
Later among the works it cites.
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Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam, · 2017
Cited alongside, same era.
“An overview of image super-resolution reconstruction algorithm,”
Xiaoming Niu, · 2018
Cited alongside, same era.
“Visual quality assessment for super-resolved images: Database and method,”
Fei Zhou, Rongguo Yao, Bozhi Liu, and Guoping Qiu, · 2019
Cited alongside, same era.
“A full-reference quality assessment metric for fine-grained compressed images,”
Zicheng Zhang, Wei Sun, Xiongkuo Min, Tao Wang, Wei Lu, and Guangtao Zhai, · 2021
Later among the works it cites.
“Deep learning based full-reference and no-reference quality assessment models for compressed ugc videos,”
Wei Sun, Tao Wang, Xiongkuo Min, Fuwang Yi, and Guangtao Zhai, · 2021
Later among the works it cites.