A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction

Jon Alvarez Justo, Daniela Lupu, Milica Orlandic, Ion Necoara, Tor Arne Johansen
1/26/2024
cs.CV

No confident code implementation found for this paper. 1 possible match can be shown below.

Abstract

Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large amount of data. This work addresses the recovery of hyperspectral images 2.5x compressed. A comparative study in terms of the accuracy and the performance of the convex FISTA/ADMM in addition to the greedy gOMP/BIHT/CoSaMP recovery algorithms is presented. The results indicate that the algorithms recover successfully the compressed data, yet the gOMP algorithm achieves superior accuracy and faster recovery in comparison to the other algorithms at the expense of high dependence on unknown sparsity level of the data to recover.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Study of the gOMP Algorithm for Recovery of Compressed Sensed Hyperspectral Images

87% match2024

Binarized Spectral Compressive Imaging

81% match2 repos1.1k2026

Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat

79% match1 repo112024

Cross-Scan Mamba with Masked Training for Robust Spectral Imaging

78% match2024

In2SET: Intra-Inter Similarity Exploiting Transformer for Dual-Camera Compressive Hyperspectral Imaging

78% match1 repo152026

Degradation-Aware Metric Prompting for Hyperspectral Image Restoration

77% match1 repo112025

Cite this paper

@article{justo2024comparative,
  title  = {A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction},
  author = {Jon Alvarez Justo and Daniela Lupu and Milica Orlandic and Ion Necoara and Tor Arne Johansen},
  year   = {2024},
  eprint = {2401.14762},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.14762v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2401.14762.svg)](https://codeofpaper.com/paper/2401.14762)

Discussion