A survey on super-resolution imaging

By Jing Tian, Kai-Kuang Ma. Signal, Image and Video Processing, 2011

310 citations recorded by the source corpus.

How researchers see this paper

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CT Super-Resolution GAN Constrained by the Identical, Residual, and Cycle Learning Ensemble (GAN-CIRCLE) (2018, background): “This computational deblurring job is a major challenge, representing a seriously ill-posed inverse problem [3], [15].”
Cardiac Image Super-Resolution with Global Correspondence Using Multi-Atlas PatchMatch (2013, methodology): “The LR images are considered to be degraded versions of the SR image undergoing blurring, downsampling and the addition of noise [6,7,4].”
Image super-resolution: The techniques, applications, and future (2016, background): “Early review papers have provided specific descriptions and explanations of those methods [4,8,14].”
Deep Learning for Image Super-Resolution: A Survey (2019, unknown): “Although there are some existing SR surveys in literature, our work differs in that we are focused in deep learning based SR techniques, while most of the earlier works [33], [34], [35], [36] aim at surveying traditional SR algorithms or some studies mainly concentrate on providing quantitative eval”