A General Framework for 1-D Histogram-baesd Image Contrast Enhancement

Ezoji, Mehdi; Iravani, Sahar

doi:10.5829/idosi.ije.2016.29.10a.09

Cited by 2 publications

(1 citation statement)

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“…Finally, convergence speed of a novel binary PSO was improved by fitness-based acceleration coefficients [22]. A new framework for image enhancement is proposed by Iravani and Ezoji [23] using optimization theorem. The quality of the image was improved by Tang et al [24], Lin and Feng [25] based on image denoising.…”

Section: Introductionmentioning

confidence: 99%

Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Rashno

Fadaei

2023

IJE

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Image restoration is the operation of obtaining a high-quality image from a corrupt/noisy image and is widely used in many applications such as Magnetic Resonance Imaging (MRI) and fingerprint identification. This paper proposes an image restoration model based on projection onto convex sets (POCS) and particle swarm optimization (PSO). For this task, a number of convex sets are used as constraints and images are projected to these sets iteratively to reach restored image. Since relaxation parameter in POCS has a significant effect on restoration results, PSO is developed to find the best value for this parameter to be used in restoration process. The proposed scheme for image restoration is evaluated on three popular images with 4 configurations of noise, compared with 5 competitive restoration models. Results demonstrate that the proposed method outperforms other models in 32 out of 48 cases in images with different noise configurations with respect to relative error, ISNR, MAE and MSE measures.

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Section: Introductionmentioning

confidence: 99%

Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Rashno

Fadaei

2023

IJE

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Digital Analysis of Geo-Referenced Concrete Scanning Electron Microscope (SEM) Images

Ahamad

Maizul

2020

Civil and Environmental Engineering Reports

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The microstructural evaluation of complex cementitious materials has been made possible by the microscopic imaging tools such as Scanning Electron Microscope (SEM) and X-Ray Microanalysis. Particularly, the application of concrete SEM imaging and digital image analysis have become common in the analysis and mapping of concrete technology. In this study, six samples of two-dimensional (2D) SEM images were spatially resampled to produce Geo-referenced SEM sample images. Subsequently, they were analyzed and the intensity histogram plot was produced to facilitate visual interpretation. The consecutive digital image analysis performed was the enhancement and noise removal process using two filtering methods i.e. median and adaptive box filter. The filtered resampled images, then undergone the unsupervised K-Means classification process to collectively separate each individual pixel corresponds to the spectral data. By spatial segmentation of K-Means algorithms, the cluster groups generated were carefully reviewed before proceeding to the final analysis. From the resulting data, the mapping of the spatial distribution of k-cluster and the quantification of micro-cracks (voids) were performed. The results of the SEM images (1st - 4th sample) showed a higher percentage of k-cluster data indicating a good correlation with the major elemental composition of EDX analysis, namely Oxide (O), Silicon (Si) and Carbon (C). Meanwhile, the subjective visual assessment of the image (5th and 6th sample) has confirmed the micro-crack developments on the concrete SEM images upon which the crack density was 3.02 % and 1.30 %, respectively.

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A General Framework for 1-D Histogram-baesd Image Contrast Enhancement

Cited by 2 publications

References 0 publications

Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Image Restoration by Projection onto Convex Sets with Particle Swarm Parameter Optimization

Digital Analysis of Geo-Referenced Concrete Scanning Electron Microscope (SEM) Images

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