Basis pursuit denoising-based image superresolution using a redundant set of atoms

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• 作者：Muhammad Sajjad ; Irfan Mehmood ; Naveed Abbas…
• 关键词：Superresolution ; Basis pursuit ; Dictionary
• 刊名：Signal, Image and Video Processing
• 出版年：2016
• 出版时间：January 2016
• 年：2016
• 卷：10
• 期：1
• 页码：181-188
• 全文大小：1,387 KB
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• 作者单位：Muhammad Sajjad (1)
  Irfan Mehmood (1)
  Naveed Abbas (2)
  Sung Wook Baik (1)
  
  1. College of Electronics and Information Engineering, Sejong University, Seoul, Korea
  2. ViCube Research Lab, Faculty of Computing, University Technology Malaysia, Johor Bahru, Malaysia
  
• 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

文摘

Digital investigations are very difficult to conduct from low-quality images generated by low-quality sensors. Therefore, we present a novel superresolution (SR) scheme that applies SR and denoising simultaneously, using the concept of sparse representation. For SR, a low-resolution (LR) input image is scaled up using our recently described adaptive interpolation scheme, and for each patch of the LR input, a vector of the sparse coefficients is then sought using a basis pursuit denoising sparse-coding algorithm instead of orthogonal matching pursuit. A high-resolution output is generated from the given LR input using the recovered vector of the sparse coefficients over a redundant set of atoms, i.e., an overcomplete dictionary. For the proposed technique, we modified the sparse-coding method of the K-SVD dictionary training approach by incorporating an efficient \(l_{1}\)-regularized least-squares method, i.e., a feature-sign search algorithm. Experimental evaluations validate the effectiveness of the proposed SR scheme. Keywords Superresolution Basis pursuit Dictionary