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http://www.hpl.hp.com/techreports/2010/HPL-2010-157.pdf
Example Based Single-frame Image Super-resolution by Support Vector Regression problem. A commonly used constrain is the smoothness of an image. In multiple frames super-resolution, more constraints are given, but the nature of problem is still ill-posed since there are different models which lead to different solutions.
https://www.researchgate.net/publication/228624667_Example_Based_Single-Frame_Image_Super-Resolution_by_Support_Vector_Regression
Recently, Dalong Li and Steven Simske [3], reported their work based on an example based single-frame image super resolution using support vector regression (SVR). Experimental results indicate ...
http://www.hpl.hp.com/techreports/2010/HPL-2010-157.html
Keyword(s): Support Vector Regression, single-frame image super- resolution, ill-posed problem, example-based, machine learning Abstract: As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in …
http://www.jprr.org/index.php/jprr/article/view/253
Example Based Single-Frame Image Super-Resolution by Support Vector Regression. Full Text . Close . ... As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning. However, the proposed method in this paper is different from other learning based ...
http://vislab.ucr.edu/PUBLICATIONS/pubs/Journal%20and%20Conference%20Papers/after10-1-1997/Conference/2011/Improved%20Image%20Super-Resolution%20by%20Support%2011.pdf
for image SR [4] [5] [6]. In [4] an example based learning algorithm is proposed by predicting the HR images from LR images via a Markov Random Field (MRF) model that is computed by belief propagation. In [5] support vector regression (SVR) is applied to single image super-resolution in Discrete Cosine Transform (DCT) domain.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.5314
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Support Vector Regression, single-frame image super-resolution, ill-posed problem, example-based, machine learning As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning.
https://www.sciencedirect.com/science/article/pii/S0167865517301241
A new in-scale single image super-resolution approach is proposed in this paper. The proposed approach effectively applies support vector regression (SVR) for learning and generates high resolution image.Cited by: 7
http://www.ijcsit.com/docs/Volume%205/vol5issue04/ijcsit2014050439.pdf
Interpolation Based Image Super Resolution by Support-Vector-Regression Sowmya. M 1, Anand M.J 2 1Final Year M.Tech Student, Department Of Electronics And Communication, PES College Of Engineering, Mandya, Karnataka, India. 2Assistant Professor, Department Of Electronics And Communication, PES College Of Engineering, Mandya, Karnataka, India. ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.730.9629
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): As many other inverse problems, single-frame image super-resolution is an ill-posed prob-lem. The problem has been approached in the context of machine learning. However, the proposed method in this paper is different from other learning based methods regarding how the input/output are formulated as well as how the ...
https://www.researchgate.net/publication/224641292_Single_Image_Superresolution_Based_on_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://www.researchgate.net/publication/228624667_Example_Based_Single-Frame_Image_Super-Resolution_by_Support_Vector_Regression
Recently, Dalong Li and Steven Simske [3], reported their work based on an example based single-frame image super resolution using support vector regression (SVR). Experimental results indicate ...
http://www.hpl.hp.com/techreports/2010/HPL-2010-157.pdf
Example Based Single-frame Image Super-resolution by Support Vector Regression problem. A commonly used constrain is the smoothness of an image. In multiple frames super-resolution, more constraints are given, but the nature of problem is still ill-posed since there are different models which lead to different solutions.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.225.5314
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Support Vector Regression, single-frame image super-resolution, ill-posed problem, example-based, machine learning As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning.
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.730.9629
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): As many other inverse problems, single-frame image super-resolution is an ill-posed prob-lem. The problem has been approached in the context of machine learning. However, the proposed method in this paper is different from other learning based methods regarding how the input/output are formulated as well as how the ...
http://www.hpl.hp.com/techreports/2010/HPL-2010-157.html
Keyword(s): Support Vector Regression, single-frame image super- resolution, ill-posed problem, example-based, machine learning Abstract: As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning.
http://www.jprr.org/index.php/jprr/article/viewFile/253/67
As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning. However, the proposed method in this paper is different from other learning based methods regarding how the input/output are formulated as well as how the learning is done.
https://www.sciencedirect.com/science/article/pii/S0167865517301241
A new in-scale single image super-resolution approach is proposed in this paper. The proposed approach effectively applies support vector regression (SVR) for learning and generates high resolution image.Cited by: 7
http://vislab.ucr.edu/PUBLICATIONS/pubs/Journal%20and%20Conference%20Papers/after10-1-1997/Conference/2011/Improved%20Image%20Super-Resolution%20by%20Support%2011.pdf
for image SR [4] [5] [6]. In [4] an example based learning algorithm is proposed by predicting the HR images from LR images via a Markov Random Field (MRF) model that is computed by belief propagation. In [5] support vector regression (SVR) is applied to single image super-resolution in Discrete Cosine Transform (DCT) domain.
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 ...
http://www.ijcsit.com/docs/Volume%205/vol5issue04/ijcsit2014050439.pdf
Interpolation Based Image Super Resolution by Support-Vector-Regression Sowmya. M 1, Anand M.J 2 1Final Year M.Tech Student, Department Of Electronics And Communication, PES College Of Engineering, Mandya, Karnataka, India. 2Assistant Professor, Department Of Electronics And Communication, PES College Of Engineering, Mandya, Karnataka, India. ...
http://www.hpl.hp.com/techreports/2011/HPL-2011-56.pdf
Support Vector Regression, single-frame image super-resolution, PSNR, PCA Support vector regression has been proposed in a number of image processing tasks including blind image deconvolution, image denoising and single frame super-resolution. As for other machine learning methods, the training is slow.
http://core.ac.uk/display/21833202
Support Vector Regression, single-frame image super-resolution, ill-posed problem, example-based, machine learning As many other inverse problems, single-frame image super-resolution is an ill-posed problem. The problem has been approached in the context of machine learning.
https://link.springer.com/chapter/10.1007%2F978-3-319-13461-1_22
Li, D., Simske, S.: Example Based Single-frame Image Super-resolution by Support Vector Regression. Journal of Pattern Recognition Research 1, 104–118 (2010) CrossRef Google Scholar 11.
http://www.docin.com/p-1464248686.html
Since example based approaches whichwe know exactly what outputshall objectivelymeasure performancesince trainedmodel “correctly”restore imagerather than image,e.g. sharpening. Keywords: Support Vector Regression, single-frame image super-resolution, ill-posed problem, example-based, machine learning.
https://www.researchgate.net/publication/271456345_Image_super-resolution_using_multi-layer_support_vector_regression
Existing support vector regression (SVR) based image superresolution (SR) methods always utilize single layer SVR model to reconstruct source image, which are incapable of restoring the details ...
https://www.infona.pl/resource/bwmeta1.element.ieee-art-000001660414
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 opposed to current classification techniques, regression allows considerably more freedom in the determination of missing high-resolution information.
https://www.mathworks.com/help/images/single-image-super-resolution-using-deep-learning.html
Introduction. Super-resolution is the process of creating high-resolution images from low-resolution images. This example considers single image super-resolution (SISR), where the goal is to recover one high-resolution image from one low-resolution image.
https://arxiv.org/pdf/1011.2272
probabilistic framework. In [30] Kwang In Kim et al proposed a regression-based method for single imag super-resolution. Kernel ridge regression (KRR) was used to estimate the high-frequency details of the underlying high-resolution image. Joshy and Choudhari have proposed a learning based method for image super resolution from zoomed observations.
https://www.sciencedirect.com/science/article/pii/S0165168416302973
Gaussian process (GP) regression is a popular statistical kernel method for learning the relationship hidden in data. However, the extensive calculation of kernel matrix hinders the further applications in many computer vision tasks such as super-resolution (SR).
https://www.groundai.com/project/super-resolution-via-deep-learning/1
In this paper we attempt to survey the deep learning literature in the context multimedia super-resolution. The main focus is on three areas, viz. still images, videos and higher dimensions, especially the range data. For each of the three, we first introduce the relevant benchmarks before reviewing the contemporary literature on deep learning based super-resolution, which is followed by a ...
https://www.sciencedirect.com/science/article/pii/S0167865520300076
A notable example of such approaches is presented in , where seventeen IQ measures are merged through a support vector regression model , trained on a set of images automatically generated and labelled. Though interesting, this approach has one main weakness due to the fact that the user is provided with a unique quality measure and the ...
https://www.groundai.com/project/temporally-coherent-gans-for-video-super-resolution-tecogan/1
Super-resolution for natural images is a classic and difficult problem in the field of image and video processing. For single image super-resolution (SISR), deep learning based methods achieve state-of-the-art peak signal-to-noise ratios (PSNR), while architectures based on Generative Adversarial Networks (GANs) achieve major improvements in terms of perceptual quality.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3296543/
Mar 01, 2012 · We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation.
https://arxiv.org/pdf/1812.09375
(a) Low-resolution frames (b) Super-resolved image Figure 1.2: Example of multi-frame super-resolution by exploiting subpixel motion across a set of low-resolution frames.(a)Sequence of low-resolution frames.(b)Super-resolved image (4 magnification) gained from 17 frames using the method proposed in Chapter4. for the overall optical resolution.
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