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http://vislab.ucr.edu/PUBLICATIONS/pubs/Journal%20and%20Conference%20Papers/after10-1-1997/Conference/2011/Improved%20Image%20Super-Resolution%20by%20Support%2011.pdf
In [5] support vector regression (SVR) is applied to single image super-resolution in Discrete Cosine Transform (DCT) domain. In [6], the SVR is applied to find the mapping between the LR images and the HR images in the spatial domain. In our approach, the SR is also formulated as a regression …
https://ieeexplore.ieee.org/document/4200763/
Image Superresolution Using Support Vector Regression Abstract: A thorough investigation of the application of support vector regression (SVR) to the superresolution problem is conducted through various frameworks. Prior to the study, the SVR problem is enhanced by finding the optimal kernel.Cited by: 269
https://www.researchgate.net/publication/6289713_Image_Superresolution_Using_Support_Vector_Regression
In this paper, a novel super-resolution face reconstruction framework based on support vector regression (SVR) about a single image is presented. Given some input data, SVR can precisely predict...
https://dl.acm.org/citation.cfm?id=2321380
After this optimization, investigation of the relevancy of SVR to superresolution proceeds with the possibility of using a single and general support vector regression for all image content, and the results are impressive for small training sets.Cited by: 269
https://www.researchgate.net/publication/224641292_Single_Image_Superresolution_Based_on_Support_Vector_Regression
Support vector machine (SVM) regression is considered for a statistical method of single frame superresolution in both the spatial and discrete cosine transform (DCT) domains. As …
https://www.researchgate.net/publication/221534545_Single_Image_Super-Resolution_Based_on_Support_Vector_Regression
Motivated by the success of support vector regression (SVR) in blind image deconvolution, we apply SVR to single-frame super-resolution. Initial results show that even when trained on as little as...
http://web.eecs.umich.edu/~cscott/past_courses/eecs545f11/projects/BlankmanMcmillanSmith.pdf
super-resolution is belief propogation where an image’s high-resolution equivalent is treated like a Markov network. Finally, support vector regression has been used for super resolution applications, most notably by Ni et al..
https://link.springer.com/chapter/10.1007/978-3-030-16681-6_21
May 21, 2019 · This paper super resolves a low resolution image to high resolution image, with the model generated from the training set using sparse online greedy support vector regression. The method is evaluated with super resolution using support vector regression. Comparisons are done on the PSNR, time and memory scales.Author: Jesna Anver, P. Abdulla
http://www.ijcsit.com/docs/Volume%205/vol5issue04/ijcsit2014050439.pdf
reconstructed from lower resolution images using Super-Resolution (SR) algorithm based on Support Vector Regression (SVR) by combining the pixel intensity values with local gradient information. Support Vector Machine (SVM) can construct a hyperplane in a high or infinite dimensional space which can be used for classification. Its regression
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