Journal of Visual Communication and Image Representation
Journal of Visual Communication and Image Representation
5 years ago

Fast BCS-FOCUSS and DBCS-FOCUSS with Augmented Lagrangian and Minimum Residual Methods

Block compressive sensing FOCal Underdetermined System Solver (BCS-FOCUSS) and distributed BCS-FOCUSS (DBCS-FOCUSS) are iterative algorithms for individual and joint recovery of correlated images. The performance of both these algorithms was noticed to be best within BCS framework. However, both these algorithms suffer from high computational complexity and recovery time. This is caused by the need for an explicit computation of matrix inverse in each iteration and a slow convergence from a poor starting point. In this paper, we propose a methodology to obtain fast and good initial solution using the augmented Lagrangian method to improve the convergence rate of both algorithms. We also propose to incorporate the minimum residual method to avoid matrix inversion to reduce the computational cost. Simulation studies with the proposed modified BCS-FOCUSS and DBCS-FOCUSS demonstrate a significant reduction in the computational cost and recovery time while improving reconstruction quality for both individual and joint reconstruction algorithms.

Publisher URL: www.sciencedirect.com/science

DOI: S1047320318300415

You might also like
Discover & Discuss Important Research

Keeping up-to-date with research can feel impossible, with papers being published faster than you'll ever be able to read them. That's where Researcher comes in: we're simplifying discovery and making important discussions happen. With over 19,000 sources, including peer-reviewed journals, preprints, blogs, universities, podcasts and Live events across 10 research areas, you'll never miss what's important to you. It's like social media, but better. Oh, and we should mention - it's free.

  • Download from Google Play
  • Download from App Store
  • Download from AppInChina

Researcher displays publicly available abstracts and doesn’t host any full article content. If the content is open access, we will direct clicks from the abstracts to the publisher website and display the PDF copy on our platform. Clicks to view the full text will be directed to the publisher website, where only users with subscriptions or access through their institution are able to view the full article.