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{ "item_title" : "Sparse Signal Processing and Compressed Sensing Recovery", "item_author" : [" Sahoo Sujit Kumar", "Makur Anamitra "], "item_description" : "The presented work revolves around sparsity. It contributes to dictionary training for sparse representation with a new algorithm and analysis. It showcases the usability of trained dictionary in image processing problems. It demonstrates a new framework for image recovery (inpainting and denoising) using sparse representation. In the end, it proposes an extension of the well-known sparse signal recovery algorithm, Orthogonal Matching Pursuit (OMP) for compressed sensing. It also provides a complete analysis of the proposed extension, and its theoretical guarantees.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/65/976/213/365976213X_b.jpg", "price_data" : { "retail_price" : "66.85", "online_price" : "66.85", "our_price" : "66.85", "club_price" : "66.85", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Sparse Signal Processing and Compressed Sensing Recovery|Sahoo Sujit Kumar

Sparse Signal Processing and Compressed Sensing Recovery

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Overview

The presented work revolves around sparsity. It contributes to dictionary training for sparse representation with a new algorithm and analysis. It showcases the usability of trained dictionary in image processing problems. It demonstrates a new framework for image recovery (inpainting and denoising) using sparse representation. In the end, it proposes an extension of the well-known sparse signal recovery algorithm, Orthogonal Matching Pursuit (OMP) for compressed sensing. It also provides a complete analysis of the proposed extension, and its theoretical guarantees.

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Details

  • ISBN-13: 9783659762130
  • ISBN-10: 365976213X
  • Publisher: LAP Lambert Academic Publishing
  • Publish Date: July 2015
  • Dimensions: 9 x 6 x 0.32 inches
  • Shipping Weight: 0.46 pounds
  • Page Count: 136

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