Single-image super-resolution using kernel recursive least squares

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• 作者：Jesna Anver ; P. Abdulla
• 关键词：Super ; resolution ; Approximate linear dependence kernel recursive least square ; Sliding window kernel recursive least square ; Kernel k ; means
• 刊名：Signal, Image and Video Processing
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：10
• 期：8
• 页码：1551-1558
• 全文大小：1,561 KB
• 刊物类别：Engineering
• 刊物主题：Signal,Image and Speech Processing
  Image Processing and Computer Vision
  Computer Imaging, Vision, Pattern Recognition and Graphics
  Multimedia Information Systems
  
• 出版者：Springer London
• ISSN：1863-1711
• 卷排序：10

文摘

Online single-image super-resolution of an image has been obtained here. The high-resolution image is constructed from a dictionary of features that approximately spans the subspace of regression. This paper classifies the low-resolution image using the kernel k-means clustering algorithm and makes an extensive study using the approximate linear dependence kernel recursive least square and sliding window kernel recursive least squares for super-resolving the image from the existing low- and high-resolution images. The super-resolution using kernel recursive least square significantly provides an improvement up on the support vector regression solution, both in terms of speed, dictionary samples and also gives a better PSNR value.