Image reconstruction using Orthogonal Matching Pursuit (OMP) algorithm

Goklani, Hemant S.; Sarvaiya, Jignesh N.; Fahad, Ahmed Mohammed

doi:10.1109/et2ecn.2014.7044960

Cited by 11 publications

(5 citation statements)

References 15 publications

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“…Sparse representation is used based on matching pursuit (MP) algorithm [23]. Hemant S.Goklani et al proposed an image reconstruction using orthogonal matching pursuit (OMP) algorithm in the presence of noise [24]. In each iteration the column that is most strongly correlated with the residue is chosen and the least square method is used to reduce the error involved.…”

Section: Literature Surveymentioning

confidence: 99%

Dictionary Learning Based Adaptive Defect Detection In Complex Fabric Textures

2023

IJCDS

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Textile industry is one of the noticeable contributors to our nation's growth. The quality control procedures in textile production primarily involves the defect detection process. For detecting the defects in complex fabric textures, proper construction of sparse representation is needed. Existing fabric defect detection methods are incapable of detecting defects in more than one type of fabric and have increased detection time while missing few defects. In this paper, dictionary learning is proposed which is used to learn the sparse representation of complex data. Three types of greedy algorithms OMP, ROMP and STOMP are used for sparse representation and the results are compared based on computational speed and accuracy. The experimental results indicate that the STOMP algorithm gives accurate and precise results with lesser time consumption. STOMP achieves 99.3% reduction in time consumption compared to OMP and 97.7% reduction in time consumption compared to ROMP. Also, if ROMP and STOMP are used for signal recovery, the formulation of joint matrix is not essential resulting in reduced computational complexity.

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Section: Literature Surveymentioning

confidence: 99%

Dictionary Learning Based Adaptive Defect Detection In Complex Fabric Textures

2023

IJCDS

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“…It can be translated into a sparse coding problem, which is finding a representation of the data as a linear combination of dictionary atoms as sparse as possible. To focus the discussion, we concentrate on the case of orthogonal matching pursuit (OMP) [23] sparse coding, which is a widely used method which is relatively simple to analyse.…”

Section: Generalised Non‐locally Centralised De‐noising With Sparsementioning

confidence: 99%

Generalised non‐locally centralised image de‐noising using sparse dictionary

Ren

et al. 2018

IET image process

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Recently, image de-noising algorithm based on sparse representation has received an increasing amount of attention. Such algorithms proposed a comprehensive sparse representation model, by solving the sparse coding problem and choosing the proper method for dictionary updating to achieve better de-noising results. Therefore, the construction of learning dictionary has become one of the key problems that limit the de-noising effectiveness. The non-locally centralised sparse representation de-noising algorithm uses principal component analysis method to achieve dictionary updating. Nevertheless, the instability of a single complete dictionary in sparse coding leads to erratic result in the process of the original image restoration. In this study, the authors present a new method named generalised non-locally centralised sparse representation algorithm. In the proposed method, the authors cluster the training patches extracted from a set of example images into subspaces, and then train dictionaries for subspaces by sparse analysis k-singular value decomposition dictionary, which is utilised to construct coded sub-block dictionary to avoid the instable results caused by a single dictionary. Experiments show that the improved method has better signal-to-noise ratio and de-noising effect compared with other methods.

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“…The calculation of ACV is based on the OMP algorithm [14]. The details of the algorithms are: Input:…”

Section: (3) Adaptive Classification Vector Calculationmentioning

confidence: 99%

Rolling bearing fault diagnosis under fluctuant conditions based on compressed sensing

Yuan

2016

Struct. Control Health Monit.

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Bearings are widely used in industries and construction for shaft supporting or seismic isolation. In recent years, their fault diagnosis, especially under variable, or fluctuant conditions, has received increasing attention. Sufficient monitoring data are usually required for bearing diagnosis. However, sufficient data cannot be guaranteed in some engineering cases with limitations of the transmission channel bandwidth or onboard/onsite computational capabilities. Fortunately, the emerging compressed sensing technique, which provides an effective solution to data compression and processing, has the ability to transform traditional monitoring data to the compressed information domain for a highly effective diagnosis under fluctuant conditions. This study proposes a bearing fault diagnosis method under fluctuant conditions based on compressed sensing theory. First, a random matrix is constructed as the measurement matrix and is employed to compress the original signal into the compressed information domain. Then, reconstruction-evaluation based fault diagnosis method is conducted with compressed signals. Moreover, the compressed signals used for fault diagnosis are reconstructed on the remote side. The experimental results provide evidence that the proposed method can effectively reduce the data volume required for bearing diagnosis and maintain an accuracy similar to current approaches, and the reconstructed signals can be used for other fault diagnosis methods.

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Image reconstruction using Orthogonal Matching Pursuit (OMP) algorithm

Cited by 11 publications

References 15 publications

Dictionary Learning Based Adaptive Defect Detection In Complex Fabric Textures

Dictionary Learning Based Adaptive Defect Detection In Complex Fabric Textures

Generalised non‐locally centralised image de‐noising using sparse dictionary

Rolling bearing fault diagnosis under fluctuant conditions based on compressed sensing

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