Optimal composite morphological supervised filter for image denoising using genetic programming: Application to magnetic resonance images

Sharif, Muhammad; Jaffar, M. Arfan; Mahmood, Muhammad Tariq

doi:10.1016/j.engappai.2013.11.011

Cited by 15 publications

(5 citation statements)

References 26 publications

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“…Many researchers used GP as an effective strategy for the purpose of removing noise from an image [22][23][24][25][26][27]. Chaudhry et al [22] proposed GP for restoring degraded images by evolving an optimal function that estimated pixel intensity.…”

Section: Gp In Image Denoisingmentioning

confidence: 99%

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A recent survey on the applications of genetic programming in image processing

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

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Genetic programming (GP) has been primarily used to tackle optimization, classification, and feature selection related tasks. The widespread use of GP is due to its flexible and comprehensible tree‐type structure. Similarly, research is also gaining momentum in the field of image processing, because of its promising results over vast areas of applications ranging from medical image processing to multispectral imaging. Image processing is mainly involved in applications such as computer vision, pattern recognition, image compression, storage, and medical diagnostics. This universal nature of images and their associated algorithm, that is, complexities, gave an impetus to the exploration of GP. GP has thus been used in different ways for image processing since its inception. Many interesting GP techniques have been developed and employed in the field of image processing, and consequently, we aim to provide the research community an extensive view of these techniques. This survey thus presents the diverse applications of GP in image processing and provides useful resources for further research. In addition, the comparison of different parameters used in different applications of image processing is summarized in tabular form. Moreover, analysis of the different parameters used in image processing related tasks is carried‐out to save the time needed in the future for evaluating the parameters of GP. As more advancement is made in GP methodologies, its success in solving complex tasks, not only in image processing but also in other fields, may increase. In addition, guidelines are provided for applying GP in image processing related tasks, the pros and cons of GP techniques are discussed, and some future directions are also set.

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Section: Gp In Image Denoisingmentioning

confidence: 99%

“…On the other hand, to remove Racian noise from Magnetic-Resonance-Imaging (MRI), an optimal composite morphological filter was generated via GP [24]. In their method, a GP individual performes morphological operations on the corrupted image to obtain an observed image.…”

Section: Gp In Image Denoisingmentioning

confidence: 99%

A recent survey on the applications of genetic programming in image processing

Khan

Qureshi

Wahab

et al. 2021

Computational Intelligence

View full text Add to dashboard Cite

show abstract

“…Using MGGP, they created an efficient model between flexural strength and ceramic porosity, and solid loading. According to Sharif et al [49] proposed a genetic programming model to reduce the magnetic resonance of composite materials. They noted that the model in GP outperformed the methods previously suggested in the literature.…”

Section: Introductionmentioning

confidence: 99%

Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming

et al. 2022

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Functionally Graded Materials (FGMs) are designed for use in high-temperature applications. Since the mass production of FGM has not yet been made, the determination of its thermo-mechanical limits depends on the compositional gradient exponent value. In this study, an efficient working model is created for the thermal stress problem of the 2D-FG plate using Multi-gene Genetic Programming (MGGP). In our MGGP model in this study, data sets obtained from the numerical analysis results of the thermal stress problem are used, and formulas that give equivalent stress levels as output data, with the input data being the compositional gradient exponent, are obtained. For the current problem, efficient models that reduce CPU processing time are obtained by using the MGGP method.

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“…Low pass filtering method can eliminate the noise effectively, but it can also make the image edge fuzzy at the same time. There are some other denoising methods based on level set, morphological filter, and Markov model [5][6][7]. To reduce Gaussian noises, many scholars have proposed a series of denoising algorithm, including improved wavelet denoising method [8], improved ICA denoising method [9,10], improved morphology denoising method [6,11], method based on neural network [12], and filtering algorithm for improved denoising method [13,14].…”

Section: Introductionmentioning

confidence: 99%

A New Image Denoising Method by Combining WT with ICA

Ruan

Zhao

Jia

et al. 2015

Mathematical Problems in Engineering

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In order to improve the image denoising ability, the wavelet transform (WT) and independent component analysis (ICA) are both introduced into image denoising in this paper. Although these two algorithms have their own advantages in image denoising, they are unable to reduce noises completely, which makes it difficult to achieve ideal effect. Therefore, a new image denoising method is proposed based on the combination of WT with ICA (WT-ICA). For verifying the WT-ICA denoising method, we adopt four image denoising methods for comparison: median filtering (MF), wavelet soft thresholding (WST), ICA, and WT-ICA. From the experimental results, it is shown that WT-ICA can significantly reduce noises and get lower-noise image. Moreover, the average of WT-ICA denoising image’s peak signal to noise ratio (PSNR) is improved by 20.54% compared with noisy image and 11.68% compared with the classical WST denoising image, which demonstrates its advantage. From the performance of texture and edge detection, denoising image by WT-ICA is closer to the original image. Therefore, the new method has its unique advantage in image denoising, which lays a solid foundation for the realization of further image processing task.

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Optimal composite morphological supervised filter for image denoising using genetic programming: Application to magnetic resonance images

Cited by 15 publications

References 26 publications

A recent survey on the applications of genetic programming in image processing

A recent survey on the applications of genetic programming in image processing

Stress Analysis of 2D-FG Rectangular Plates with Multi-Gene Genetic Programming

A New Image Denoising Method by Combining WT with ICA

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