1. Image super resolution using sparse neighbor embedding and clustering algorithm
Authors : Dr.dharmanna L, Deekshith K
Pages : 616-621
DOI : http://dx.doi.org/10.21172/1.81.081
Keywords : Super Resolution, Sparse neighbor embedding, histograms of oriented gradients, K-Means clusters, K-NN Classification Abstract :This paper presents a new approach to image super resolution, based upon Sparse Neighbor Embedding and clustering algorithm .Consider a low resolution image as input. Then do LR patch separation, followed by feature extraction using histograms of oriented gradients (HOG). Then perform classification using K-means clustering algorithm. Then perform Sparse Neighbor Embedding. Finally synthesize HR patches and subject it to de-blurring algorithm to obtain a Super-Resolution Image.
Citing this Journal Article :Dr.dharmanna L, Deekshith K, "Image super resolution using sparse neighbor embedding and clustering algorithm", Volume 8 Issue 1 - January 2017, 616-621
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