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Related Experiment Video

Updated: Aug 5, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

470

Framework for Segmented threshold ℓ0 gradient approximation based network for sparse signal recovery.

Vivekanand V1, Deepak Mishra2

  • 1Vikram Sarabhai Space Centre, Indian Space Research Organisation, India; Indian Institute of Space Science and Technology, Department of Space, Thiruvananthapuram, India.

Neural Networks : the Official Journal of the International Neural Network Society
|March 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for sparse signal recovery using approximate matrix inverses and iterative thresholding. This method improves computational efficiency and allows for experimentation with different inverse matrices for better performance.

Keywords:
norm minimizationBasis function networkPolynomial approximationSparse recoveryThresholding

Related Experiment Videos

Last Updated: Aug 5, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
04:48

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Published on: July 5, 2024

470

Area of Science:

  • Signal Processing
  • Compressed Sensing
  • Numerical Analysis

Background:

  • Compressed sensing requires iterative methods for signal reconstruction due to non-invertible measurement matrices.
  • Iterative thresholding and L0 minimization are key for achieving sparse solutions but necessitate matrix inverse operations.
  • Estimating reconstruction error typically involves using the pseudo-inverse, which can be computationally intensive.

Purpose of the Study:

  • To present a novel sparse signal recovery framework utilizing an approximate inverse matrix (Q).
  • To develop efficient algorithms for sparse signal reconstruction based on iterative segment thresholding.
  • To enable flexible experimentation with different inverse matrices for enhanced recovery performance.

Main Methods:

  • Developed a framework employing an approximate inverse matrix Q for sparse signal recovery.
  • Implemented iterative segment thresholding for L0 and L1 norms with residue addition.
  • Created two recovery algorithms, including an L0-based method extended to a basis function dictionary network.

Main Results:

  • The proposed framework facilitates the use of approximate inverses, reducing computational complexity.
  • Two distinct algorithms were developed, demonstrating the framework's versatility.
  • The L0-based method was successfully adapted into a dictionary-based network for advanced sparse recovery.

Conclusions:

  • The presented framework offers a computationally efficient approach to sparse signal recovery.
  • It allows for adaptable inverse matrix selection, potentially improving reconstruction accuracy and efficiency.
  • The developed algorithms provide practical tools for signal reconstruction in compressed sensing applications.