Super-resolution image reconstruction: a technical overview

By Sung Cheol Park, Min Kyu Park, Moon Gi Kang. 2003

3,587 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Generative Adversarial Networks (2021, neutral): “The task has received substantial attention from researchers in the computer vision community for its wide downstream applications, such as image reconstruction [5].”
On a Link Between Kernel Mean Maps and Fraunhofer Diffraction, with an Application to Super-Resolution Beyond the Diffraction Limit (2013, background): “This type of superresolution is not the topic of this paper so we refer the reader to the review of Park, Park and Kang [20].”
Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation (2018, background): “2D Super-Resolution Super-resolution of 2D images is a well-studied problem [29].”
Exact Support Recovery for Sparse Spikes Deconvolution (2013, neutral): “We refer to the review articles [27, 24] and the references therein for an overview of these problems.”
Robust Super-resolution Volume Reconstruction from Slice Acquisitions: Application to Fetal Brain MRI (2010, methodology): “Regularization is typically interpreted as incorporating image priors in the reconstruction problem, which can be formulated as a maximum a posterior (MAP) estimation [15].”
Quantum metrology and its application in biology (2014, methodology): “Such an estimation procedure is a class of image deconvolution [88] (see Section 5.”
A Critical Comparison Among Pansharpening Algorithms (2015, background): “More recent improvements have been achieved through the application of superresolution techniques, which are already largely diffused in many image processing and computer vision applications [31].”
Towards a Mathematical Theory of Super‐resolution (2012, background): “For a survey of super-resolution techniques in imaging, see [31] and the references therein.”
Deep Learning for Single Image Super-Resolution: A Brief Review (2018, background): “Super-resolution (SR) [9] refers to the task of restoring highresolution images from one or more low-resolution observations of the same scene.”
Toward Real-World Single Image Super-Resolution: A New Benchmark and a New Model (2019, background): “es. 1. Introduction Single image super-resolution (SISR) [15] aims to recover a high-resolution (HR) image from its low-resolution (LR) observation. SISR has been an active research topic for decades [38,57,45,47,5] because of its high practical values in enhancing image details and textures. Since ”
Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation (2016, neutral): “In multi-image SR [28] it is assumed that different observations of the same scene are available, hence the shared explicit redundancy can be used to constrain the problem and attempt to invert the downscaling process directly.”
MADNet: A Fast and Lightweight Network for Single-Image Super Resolution (2020, background): “Addressing SISR has proven to be useful in many practical cases, such as video streaming [44], [50]; remote sensing [16], [58]; and medical imaging [31], [45], [48].”
On Bayesian Adaptive Video Super Resolution (2014, neutral): “We refer readers to [26] for a comprehensive literature review.”
The Light Field Camera: Extended Depth of Field, Aliasing, and Superresolution (2012, background): “This approach relates to a large bulk of image processing literature [14], [15], [16], [17], [18], [19].”
Ieee Transactions on Pattern Analysis and Machine Intelligence 1 on Bayesian Adaptive Video Super Resolution (neutral): “We refer readers to [26] for a comprehensive literature review.”
Super-Resolution from Noisy Data (2012, neutral): “In electronic imaging, photon shot noise limits the pixel size, making super-resolution techniques necessary to recover sub-pixel details [28, 30].”
A Super-Resolution Framework for 3-D High-Resolution and High-Contrast Imaging Using 2-D Multislice MRI (2009, background): “because of improved computational feasibility of these demanding, usually iterative, methods [16].”
Analyzing Perception-Distortion Tradeoff using Enhanced Perceptual Super-resolution Network (2018, background): “Though there exist extensive literature studies on multi-image SR [6,38,14], here we limit our discussions to SISR works alone.”