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Recovery of missing samples using sparse approximation via a convex similarity measure

Javaheri, A ; Sharif University of Technology

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  1. Type of Document: Article
  2. DOI: 10.1109/SAMPTA.2017.8024455
  3. Abstract:
  4. In this paper, we study the missing sample recovery problem using methods based on sparse approximation. In this regard, we investigate the algorithms used for solving the inverse problem associated with the restoration of missed samples of image signal. This problem is also known as inpainting in the context of image processing and for this purpose, we suggest an iterative sparse recovery algorithm based on constrained l1-norm minimization with a new fidelity metric. The proposed metric called Convex SIMilarity (CSIM) index, is a simplified version of the Structural SIMilarity (SSIM) index, which is convex and error-sensitive. The optimization problem incorporating this criterion, is then solved via Alternating Direction Method of Multipliers (ADMM). Simulation results show the efficiency of the proposed method for missing sample recovery of 1D patch vectors and inpainting of 2D image signals. © 2017 IEEE
  5. Keywords:
  6. Image processing ; Iterative methods ; Optimization ; Problem solving ; Recovery ; Alternating direction method of multipliers ; L1-norm minimizations ; Optimization problems ; Sample recoveries ; Similarity measure ; Sparse approximations ; Sparse recovery ; Structural similarity indices (SSIM) ; Inverse problems
  7. Source: 2017 12th International Conference on Sampling Theory and Applications, SampTA 2017, 3 July 2017 through 7 July 2017 ; 2017 , Pages 543-547 ; 9781538615652 (ISBN)
  8. URL: https://ieeexplore.ieee.org/document/8024455