Image enhancement algorithm using adaptive fractional differential mask technique

Zhang, Xuefeng; Hui, Yan

doi:10.3934/mfc.2019022

Cited by 11 publications

(6 citation statements)

References 35 publications

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“…The proposed method achieves the best Brisque and Piqe for almost all of the images from the two datasets. However, for the brain MRI images, the performance of the proposed method degrades slightly compared to the Zhang et al (19) method. This might be caused by the increased complexity of the said images compared to the ones found in the other two datasets, and might constitute a primary limitation of the present study.…”

Section: Resultsmentioning

confidence: 88%

“…The reason is that the proposed method uses the fractional partial differential enhanced operator which captures the high frequency image details in, regardless of noise, blur, and distortion created by image capturing systems. In the case of the existing method, it could be seen that the Fu et al (16) Figure 3E, and Zhang et al (19) Figure 3G methods produce over-enhanced images, while the proposed method produces natural appearance by enhancing the dark areas and maintaining the bright areas of input images.…”

Section: Resultsmentioning

confidence: 96%

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A medical image enhancement based on generalized class of fractional partial differential equations

Ibrahim¹,

Jalab²,

Karim³

et al. 2022

Quant Imaging Med Surg

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Background: The interest in using fractional calculus operators has grown in the field of image processing.Image enhancement is one of image processing tools that aims to improve the details of an image. The enhancement of medical images is a challenging task due to the unforeseeable variation in the quality of the captured images. Methods: In this study, we present a mathematical model based on the class of fractional partial differential equations (FPDEs). The class is formulated by the proportional-Caputo hybrid operator (PCHO). Moreover, some properties of the geometric functions in the unit disk are applied to determine the upper bound solutions for this class of FPDEs. The upper bound solution is indicated in the relations of the general hypergeometric functions. The main advantage of FPDE lies in its capability to enhance the low contrast intensities through the proposed fractional enhanced operator.Results: The proposed image enhancement algorithm is tested against brain and lungs computed tomography (CT) scans datasets of different qualities to show that it is robust and can withstand dramatic variations in quality. The quantitative results of Brisque, Piqe, SSEQ, and SAMGVG were 40.93%, 41.13%, 66.09%, and 31.04%, respectively for brain magnetic resonance imaging (MRI) images and 39. 07, 41.33, 30.97, and 159.24 respectively for the CT lungs images. The comparative results show that the proposed image enhancement model achieves the best image quality assessments.Conclusions: Overall, this model significantly improves the details of the given datasets, and could potentially help the medical staff during the diagnosis process.

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Section: Resultsmentioning

confidence: 88%

Section: Resultsmentioning

confidence: 96%

A medical image enhancement based on generalized class of fractional partial differential equations

Ibrahim¹,

Jalab²,

Karim³

et al. 2022

Quant Imaging Med Surg

View full text Add to dashboard Cite

show abstract

“…When the pixels in the asphalt concrete pavement crack image signal are adjacent in the spatial domain, they show a strong correlation, which contains a lot of structural information about the target image [13,14]. According to this information, the image is divided into image blocks with similar structure information, then the fractional order differential order is set according to the proportion of each feature image block and, finally, the order is substituted into the mask operator for calculation.…”

Section: Fractional Differential Image Feature Enhancement Algorithm ...mentioning

confidence: 99%

An Improved Crack Identification Method for Asphalt Concrete Pavement

Yang

2023

Applied Sciences

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The results of high-precision asphalt concrete pavement crack identification can provide help for pavement maintenance. Therefore, methods of image feature enhancement and crack identification of asphalt concrete pavement cracks are proposed. First of all, we used an industrial CCD camera mounted on a vehicle to collect an asphalt concrete pavement crack image. Then, after using the NeighShrink algorithm to denoise the acquired image, a fractional differential image enhancement algorithm was designed based on image feature blocks to enhance the image features. On this basis, crack characteristics were segmented and processed by watershed algorithm. Through crack direction identification and crack parameter extraction, crack distribution direction, crack length and width and other parameters of asphalt concrete pavement were obtained in order to achieve accurate identification of asphalt concrete pavement cracks. The experiment found that this method can effectively remove noise information from asphalt concrete crack images; after applying this method, the image entropy value of each image was improved, with a minimum improvement of 0.38 and a maximum improvement of 1.98. The time consumed by this method in identifying cracks in asphalt concrete pavement varied between 1.4 s and 2.4 s. When identifying the length of cracks in asphalt concrete pavement, the maximum deviation value was only 0.47 mm; when identifying the width of cracks in asphalt concrete pavement, the maximum deviation value was only 0.31 mm. The above results indicate that by enhancing the image features of asphalt concrete cracks, this method achieves more accurate identification results for crack distribution direction, length and width values, with high identification efficiency and good application effect.

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“…The experimental results on various low light datasets show the effectiveness of this method. Using the same approach, Zhang et al [24] have developed an adaptive fractional order image enhancement method based on the information of fractional order differential and the image segmentation. The image enhancement depends on the result of the image segmentation algorithm which is used to segment the image into the objects and the background.…”

Section: Related Workmentioning

confidence: 99%

Mathematical Design Enhancing Medical Images Formulated by a Fractal Flame Operator

Ibrahim¹,

Abdul-Wahed²,

Mohammed³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Image enhancement algorithm using adaptive fractional differential mask technique

Cited by 11 publications

References 35 publications

A medical image enhancement based on generalized class of fractional partial differential equations

A medical image enhancement based on generalized class of fractional partial differential equations

An Improved Crack Identification Method for Asphalt Concrete Pavement

Mathematical Design Enhancing Medical Images Formulated by a Fractal Flame Operator

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