Searching for Image Superresolution Using Support information? Find all needed info by using official links provided below.
https://www.researchgate.net/publication/6289713_Image_Superresolution_Using_Support_Vector_Regression
Image Superresolution Using Support Vector Regression Article in IEEE Transactions on Image Processing 16(6):1596-610 · July 2007 with 154 Reads How we measure 'reads'
https://www.semanticscholar.org/paper/Face-image-super-resolution-through-support-Jiang-Hu/c397408e784004240e866d0f31cea7b9e44fdd0c
HighlightsA face image super-resolution method is proposed using Locality-induced Support Regression (LiSR).The relationship between the LR and HR patches is learned on the support LR/HR pairs.It utilized the locality of patch manifold to define the support.An iterative optimization method is designed to gradually improve the target HR image.
https://ieeexplore.ieee.org/document/4200763/
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://in.mathworks.com/help/images/single-image-super-resolution-using-deep-learning.html
Then, go directly to the Perform Single Image Super-Resolution Using VDSR Network section in this example. Use the helper function, downloadIAPRTC12Data, to download the data. This function is attached to the example as a supporting file.
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…
https://arxiv.org/abs/1807.02758
Jul 08, 2018 · Convolutional neural network (CNN) depth is of crucial importance for image super-resolution (SR). However, we observe that deeper networks for image SR are more difficult to train. The low-resolution inputs and features contain abundant low-frequency information, which is treated equally across channels, hence hindering the representational ability of CNNs. To …Cited by: 60
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