Super-Resolution Imaging

By Bruce H. Pillman. J. Electronic Imaging, 2013

362 citations recorded by the source corpus.

This book serves as an introduction to the flourishing field of super-resolution imaging. It is a compiled volume, with different authors for each of its 14 chapters. While not having a strong outline or textbook format, the chapters group into several sections.

Subfields: Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/18365003485d669c000fb1b1993efba7e5c7e447

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Frame-Recurrent Video Super-Resolution (2018, methodology): “We refer the reader to Milanfar [28] and Nasrollahi and Moeslund [29] for extensive overviews of prior art up to recent years.”
Nonparametric Blind Super-resolution (2013, methodology): “Most SR methods (e.g. [11] [7] [2] [4] [20] [16] [15] [21]) assume that the low-resolution input image was obtained by down-sampling the unknown high-resolution image with a known blur kernel.”
Reconstruction of fetal brain MRI with intensity matching and complete outlier removal (2012, methodology): “More recently, super-resolution methods (Milanfar, 2007; Greenspan, 2009) in combination with slice-tovolume registration have been proposed to reduce the blurring effects during reconstruction of thick-slice data (Gholipour and Warfield, 2009; Gholipour et al.”
Fast Direct Super-Resolution by Simple Functions (2013, background): “certain mathematical formulations [13, 11] such as bicubic interpolation and back-projection [8].”
EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (2016, result): “A large number of high-performing algorithms have since been proposed [35], see also the recent surveys by Nasrol-lahi and Moeslund [37] and Yang et al .”
Super-resolution: a comprehensive survey (2014, background): “Over the past two decades, many research papers, books [101,294,534] and PhD theses [4,78,84,148,182,261,263,”