Using Dynamic Multi-Swarm Particle Swarm Optimizer to Improve the Image Sparse Decomposition Based on Matching Pursuit

Chen, Chen; Qu, Boyang; Niu, Ben

doi:10.1007/978-3-642-39482-9_68

Cited by 4 publications

(5 citation statements)

References 14 publications

(16 reference statements)

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“… Peak Signal to Noise Ratio (PSNR): The Peak Signal to Noise Ratio (PSNR) is used to measure the performance and it is defined as (Chen et al, 2013):…”

Section: Numerical Resultsmentioning

confidence: 99%

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Biomedical images enhancement based on swarm optimization and differential evolution technique

Alotaibi¹

2018

Int. j. adv. appl. sci.

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In this paper, we have introduced an effective technique to remove the noise in the MRI and CT medical images during the process of acquisition, transmission, storage or compression. Removing these noise from medical images must be done without affecting relevant features of the image. Many techniques, such as genetic algorithm, Particle Swarm Optimization, Dynamic Multi-Swarm Particle Swarm Optimization and matching pursuit algorithm are used for denoising MRI and CT images. These techniques need more time to remove noise from medical images and find local point optimal. The proposed Differential Evolution based on Matching Pursuit (DE-MP) is used to detect best atom dictionary. The initial dictionary is created from an anisotropic atom. To evaluate the performance of the proposed techniques, the results of the proposed algorithm were compared with the other algorithm. The numerical results show that the performance of the proposed algorithm is more efficient and faster than other algorithms for medical images denoising.

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“… Peak Signal to Noise Ratio (PSNR): The Peak Signal to Noise Ratio (PSNR) is used to measure the performance and it is defined as (Chen et al, 2013):…”

Section: Numerical Resultsmentioning

confidence: 99%

“…In the DMS-PSO, the population is divided into several small groups. In Chen et al (2013), the DMS-PSO-MP was developed by Discrete Coefficient Mutation (DCM) strategy to improve the local searching ability of DMS-PSO in the MP approach over the anisotropic atom dictionary.…”

Section: Introductionmentioning

confidence: 99%

Biomedical images enhancement based on swarm optimization and differential evolution technique

Alotaibi¹

2018

Int. j. adv. appl. sci.

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“…These atoms can be found in each iteration. For an arbitrary image of size × , let {d γ } γ∈Γ are the atoms of the dictionary , where is the set of all indexes and ||d γ || = 1 [21]. The approximation of by projecting it on a vector 0 ∈ is the first step of the MP.…”

Section: Matching Pursuit (Mp)mentioning

confidence: 99%

“…Where the projection of into the atom 0 is < , 0 > 0 , and the residual of the original image is , where the is orthogonal to 0 [21]: 2 (2)…”

Section: Matching Pursuit (Mp)mentioning

confidence: 99%

A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

Elsayed¹,

Salem²,

Aly³

2020

IJIES

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Noise can affect images while acquired, transmitted, stored or compressed. One of the best methods for noise removal is the sparse representation algorithm (SR). The Quantum Particle Swarm Optimization (QPSO) is one of the meta-heuristic algorithms. This paper shows excellent results in noise reduction in the quick version of QPSO, which uses benefit of the SRs and meta-heuristic algorithms. This approach is known as FQPSO-MP, depending on the matching pursuit algorithm (MP). A proposed Dynamic-Multi-Swarm (DMS) and a pre-learned dictionary (FQPSO-MP) method saves the time of calculating the learning dictionary. These modifications contribute to important benefits of computing efficiency (productivity improvements of approximately 90% are achieved) without sacred image quality in comparison with the initial QPSO-MP technique (the bigger reduction relative to the PSNR indexes is lower than 0.58 dB and 0.019). The proposed FQPSO-MP method compared to the original QPSO-MP method after modification. The scientific results show that the FQPSO-MP algorithm is more effective and quicker without sacrificing image quality than the FQPSO-MP algorithm. The experimental results show, in comparison to state-of-the-art denoising algorithms, that both quantitative and image quality results are achieved with the suggested FQPSO-MP method.

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“…27,28 MSO is recommended for multimodal optimization problems and it is applied successfully to solve image processing problems. 29,30 CSA is also applied in image processing problems. [31][32][33][34][35] Other image and intelligent processing methods proposed in the literature, which are or can be successfully applied in the medical domain, are: echocardiograms analysis using the Fuzzy Gravitational Search algorithm to find the optimal architecture of the modular neural networks 36,37 ; electrocardiogram signals classification using competitive neural networks with the Learning Vector Quantization algorithm 38 ; cardiac arrhythmia classification with Fuzzy K-Nearest Neighbors and neural networks 39 ; and criterion for efficient estimation of areas in noisy digital images based on the usage of the Lin's concordance correlation coefficient.…”

Section: A Comparison Of Nature-inspired Optimization Algorithmsmentioning

confidence: 99%

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

Bejinariu

Costin

2018

Methods Inf Med

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Computational Intelligence Re-meets Medical Image Processing Analysis of Machine Learning Algorithms for Diagnosis of Diffuse Lung Diseases Background In the last decades, new optimization methods based on the nature's intelligence were developed. These metaheuristics can find a nearly optimal solution faster than other traditional algorithms even for high-dimensional optimization problems. All these algorithms have a similar structure, the difference being made by the strategies used during the evolutionary process. Objectives A set of three nature-inspired algorithms, including Cuckoo Search algorithm (CSA), Particle Swarm Optimization (PSO), and Multi-Swarm Optimization (MSO), are compared in terms of strategies used in the evolutionary process and also of the results obtained in case of particular optimization problems. Methods The three algorithms were applied for biomedical image registration (IR) and compared in terms of performances. The expected geometric transform has seven parameters and is composed of rotation against a point in the image, scaling on both axis with different factors, and translation. Results The evaluation consisted of 25 runs of each IR procedure and revealed that (1) PSO offers the most precise solutions; (2) CSA and MSO are more stable in the sense that their solutions are less scattered; and (3) MSO and PSO have a higher convergence speed. Conclusions The evaluation of PSO, MSO, and CSA was made for multimodal IR problems. It is possible that for other optimization problems and also for other settings of the optimization algorithms, the results can be different. Therefore, the nature-inspired algorithms demonstrated their efficacy for this class of optimization problems.

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Using Dynamic Multi-Swarm Particle Swarm Optimizer to Improve the Image Sparse Decomposition Based on Matching Pursuit

Cited by 4 publications

References 14 publications

Biomedical images enhancement based on swarm optimization and differential evolution technique

Biomedical images enhancement based on swarm optimization and differential evolution technique

A Fast Quantum Particle Swarm Optimization Algorithm for Image Denoising Problem

A Comparison of Some Nature-Inspired Optimization Metaheuristics Applied in Biomedical Image Registration

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