A Review on Image Reconstruction Using Compressed Sensing Algorithms: OMP, CoSaMP and NIHT

Goklani, Hemant S.

doi:10.5815/ijigsp.2017.08.04

Cited by 7 publications

(2 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several variants of the OMP algorithm in the literature try to reduce its computational time due to its nature of NP-hard problem. 14 The difference in these variants turns around the computation of the matrix inversion of the dictionary. 15 For the proposed implementation, the algorithm employed is OMP by Cholesky Decomposition.…”

Section: Merge Frames Into Videomentioning

confidence: 99%

Real-time video super-resolution reconstruction using wavelet transforms and sparse representation

Mora-Martinez,

Ponomaryov,

Garcia-Salgado

et al. 2023

Real-Time Processing of Image, Depth and Video Information 2023

View full text Add to dashboard Cite

Video super-resolution reconstruction consists of generating high-resolution frames by processing low-resolution ones. This process enhances the video quality, allowing the visualisation of fine details. Moreover, it can be considered a primary step in a video processing pipeline for further applications, such as object detection, classification and tracking from uncrewed aerial vehicles (UAV). For this reason, the super-resolution process should be performed quickly and accurately. Implementing a real-time video super-resolution method through parallel programming contributes to the efficiency of this pipeline. This work proposes two parallel super-resolution approaches for videos taken from UAVs: one using multi-core CPUs and another on a GPU architecture. The method is based on sparse representation and Wavelet transforms. First, it makes an edge correction performed in the Wavelet domain, then employs dictionaries previously trained with k-Singular Value Decomposition (k- SVD) to reconstruct the Wavelet subbands of the frames, and the high-resolution frames are computed from the Inverse Discrete Wavelet Transform (IDWT). The performance of this method was measured with the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM) and Edge Preservation Index (EPI). The implementations are tested in a workstation with a Ryzen multi-core processor and a CUDA-enabled GPU; furthermore, they are compared with the non-parallel method regarding algorithm complexity and computing time.

show abstract

Section: Merge Frames Into Videomentioning

confidence: 99%

Real-time video super-resolution reconstruction using wavelet transforms and sparse representation

Mora-Martinez,

Ponomaryov,

Garcia-Salgado

et al. 2023

Real-Time Processing of Image, Depth and Video Information 2023

View full text Add to dashboard Cite

show abstract

“…However, the SAMP algorithm may cause underestimation or overestimation. Compressive sampling matching pursuit (CoSaMP) [21][22][23][24] is an improved orthogonal matching pursuit (OMP) algorithm, and it is very effective for both sparse and common signals. In channel estimation, it still needs channel sparsity, which is practically impossible to gather.…”

Section: Introductionmentioning

confidence: 99%

A Sparse CoSaMP Channel Estimation Algorithm With Adaptive Variable Step Size for an OFDM System

Xiaoling,

Yangyi,

Linsen

2024

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Compressive sampling matching pursuit (CoSaMP), as a conventional algorithm requiring system sparsity and sensitive to step size, was improved in this paper by approximating the sparsity with adaptive variable step size. In the proposed algorithm (CoSaMP with variable step size abbreviated as Vss-CoSaMP), the idea of approximating sparsity with adaptive step size was borrowed from the sparsity adaptive matching pursuit (SAMP) algorithm to determine the sparsity for the CoSaMP algorithm. The applicability of the CoSaMP algorithm was therefore expanded considerably. On this basis, a step size reduction was added as the iteration termination condition of an orthogonal frequency division multiplexing (OFDM) system. An adaptive variable step size algorithm was then put forward to address the CoSaMP algorithm’s sensitivity to step size. It could realize the required precision at different initial step sizes. A simulation was carried out to analyze the influence of pilot number and step size in an OFDM system on the algorithm. The algorithms, including SAMP, CoSaMP, and Vss-CoSaMP, were compared with two sparse channels, revealing that the Vss-CoSaMP algorithm overcame the problem of the CoSaMP algorithm, that is, the impossibility to forecast the channel sparsity. With the adaptive step size, the proposed algorithm could reach and achieve better accuracy than the CoSaMP algorithm. Additionally, the proposed algorithm was superior over the SAMP algorithm in terms of reconstruction, mean square error (MSE), and bit error ratio (BER).

show abstract

Video Anomaly Detection Based on Hierarchical Clustering

Zhao

Wang

et al. 2019

Human Centered Computing

View full text Add to dashboard Cite

A Review on Image Reconstruction Using Compressed Sensing Algorithms: OMP, CoSaMP and NIHT

Cited by 7 publications

References 20 publications

Real-time video super-resolution reconstruction using wavelet transforms and sparse representation

Real-time video super-resolution reconstruction using wavelet transforms and sparse representation

A Sparse CoSaMP Channel Estimation Algorithm With Adaptive Variable Step Size for an OFDM System

Video Anomaly Detection Based on Hierarchical Clustering

Contact Info

Product

Resources

About