Single image super-resolution using Gaussian process regression

By He He, Wan-Chi Siu. 2011

306 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

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Citations in the corpus

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (2016, background): “ndency of neighborhood approaches to overﬁt and formulate a more general map of example pairs using kernel ridge regression. The regression problem can also be solved with Gaussian process regression [27], trees [46] or Random Forests [47]. In Dai et al. [6] a multitude of patch-speciﬁc regressors i”
Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (2016, neutral): “Recent popular SISR methods can be classified into edge-based [35], image statisticsbased [9, 18, 46, 12] and patch-based [2, 43, 52, 13, 54, 40, 5] methods.”
Accurate Blur Models vs. Image Priors in Single Image Super-resolution (2013, background): “A few single-image SR works which do attempt to estimate or take the unknown kernel into account include [3, 24, 17, 10, 11, 12].”
Plug-and-Play ADMM for Image Restoration: Fixed-Point Convergence and Applications (2016, methodology): “In this case, Ours-M* still performs better than the others for configuration 3, but slightly worse than GPR [11] for configuration 4.”
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations (2017, background): “Existing literatures have considered two types of downsamplers, including direct downsampler [11, 17, 36, 51, 55] and bicubic downsampler [7, 12, 14, 15, 47, 52].”
Blind Super-Resolution With Iterative Kernel Correction (2019, methodology): “Most existing blind SR methods are model-based [3, 32, 11, 12, 14], which usually involve complicated optimization procedures.”
Multi-scale dictionary for single image super-resolution (2012, methodology): “Figure 8 demonstrate more challenging results (×4 magnification) on four real LR images with rich textures and salient edges in comparison to Gaussian process regression (GPR)based SR approach in [9].”
Online Sparse Multi-Output Gaussian Process Regression and Learning (2019, background): “GP regression has found diverse applications in indoor localization [3]–[5], image processing and visual tracking [6], [7], change point detection [8] and sensor (robotic) network management [9], [10].”
Super-resolution: a comprehensive survey (2014, unknown): “8 [57], [71], [82], [99], [100], [164], [187], [200], [207], [217], [235], [274], [287], [367], [373], [392], [454], [467], [520], [524], [545], [556], [559], [578], [594], [599]”
Single-Image Super-Resolution: A Benchmark (2014, methodology): “Various learning methods of the mapping functions have been proposed such as weighted average [31,2], kernel regression [17], support vector regression [23], Gaussian process regression [13], sparse dictionary representation [46,7,5,24,45,39,47,19,14].”
Blind Super-Resolution Kernel Estimation using an Internal-GAN (2019, methodology): “Some Blind-SR methods [33, 17, 14, 13, 3] were introduced prior to the deep learning era.”