Fractional Fourier Transform based Riesz fractional derivative approach for edge detection and its application in image enhancement

Kaur, Kanwarpreet; Jindal, Neeru; Singh, Kulbir

doi:10.1016/j.sigpro.2020.107852

Cited by 42 publications

(22 citation statements)

References 27 publications

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“…2 shows the fractional Fourier transform. This approach is widely used in optical problems (Hricha et al, 2020;Saad et al, 2018;Zhao et al, 2018;Dai et al, 2021), image processing (Faragallah et al, 2021;Kaur et al, 2021;Massihi and Rashidi, 2021;Zhang et al, 2021), robotics (Lopez et al, 2021), and medical signal processing (Gupta et al, 2021;Alqahtani et al, 2022;Mastromichalakis and Chountasis, 2021).…”

Section: Fractional Fourier Transformmentioning

confidence: 99%

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

2022

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Super-resolution imaging is a critical image processing stage that improves visual image quality. Super-resolution imaging has a wide array of use in different fields, such as medical, satellite, and astronomical images. The correct execution of this stage could increase the accuracy and quality of any available processes in different executive fields. Learning methods, especially deep learning, have become much more popular in recent years for performing the super-resolution imaging process. Methods with this approach have high-quality levels but lack appropriate performance times. This study intends to perform super-resolution imaging using an algorithmic approach based on the particle swarm optimization algorithm and the fractional Fourier transform. The test results on a dataset show the 92.16% accuracy of this proposed method.

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Section: Fractional Fourier Transformmentioning

confidence: 99%

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

2022

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“…At present, many other classification methods have different advantages in image classification field. For instance, one of the commonly used methods, Discrete Wavelet Transform [10], has the defects of optimal selection of wavelet function and decomposition level [11]. Although it has more strength in the field of image categorization, FRFE is chosen as the feature vector of image preprocessing in the experiment with better performance and precision [12].…”

Section: Gingival Image Feature Extractionmentioning

confidence: 99%

Gingivitis detection by Fractional Fourier Entropy and Particle Swarm Optimization

Yan¹

2021

EAI Endorsed Transactions on e-Learning

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INTRODUCTION: we propose a detection model of gingivitis based on feature extraction based on particle swarm optimization neural network with fractional Fourier entropy. OBJECTIVES: For the sake of reduce the diagnostic burden of doctors' frequent and high concentration. METHODS: Primarily, Fourier transform is applied to the collected image signal, and the entropy is extracted from the gingival image by Shannon entropy so as to get the input value. The particle swarm optimization algorithm was combined with the extraction eigenvector is used to detect whether patients have gingivitis in final. RESULTS: The experimental results show that this method can reduce the unnecessary space of image detection, reduce the complexity of image information, and achieve a sensitivity of 79.00±1.61%, specificity of 80.89±1.87% and accuracy of 79.94±0.96%. CONCLUSION: This optimized algorithm can effectively and accurately cluster the sample data, and the accuracy is also higher than the advanced gingival image diagnosis method, making the gingivitis diagnosis more accurate. Our neural network has good trainability and recognition ability, which makes a unique contribution to medically intelligent detection methods for gingival treatment.

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“…[3][4][5]. In these applications, UAV image segmentation, and its indispensable part, image edge detection [6] are both important demand from the users. However, the existing UAV image processing solutions [7,8] may not achieve the ideal goal that well balances the above two mentioned demands.…”

Section: Introductionmentioning

confidence: 99%

Quaternion‐based improved cuckoo algorithm for colour UAV image edge detection

Liu

2021

IET Image Processing

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With the extensive application of unmanned aerial vehicles (UAVs), there is an increasing demand for fast processing of coloured UAV images. The coloured UAV image pixels are usually represented by quaternion vectors with three bands of visible light corresponding to the three imaginary parts of the pure imaginary quaternion. Accordingly, the colour image edge points can be determined based on the quaternion polar coordinating the rotation principle. Here, a quaternion‐based improved cuckoo algorithm is proposed to perform fast processing for UAVs images. In particular, a novel guiding equation is used to optimize the positions of the improved cuckoo algorithm before the Levi flight. Furthermore, a novel disturbance equation is used to obtain a varied location for the next location after the Levi flight. Comprehensive experiments are conducted to evaluate the performance of the proposed solution. The experimental results showed that the proposed method significantly reduces the image processing time and remarkably improves the quality.

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Fractional Fourier Transform based Riesz fractional derivative approach for edge detection and its application in image enhancement

Cited by 42 publications

References 27 publications

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

Adaptive Image Super-Resolution Algorithm Based on Fractional Fourier Transform

Gingivitis detection by Fractional Fourier Entropy and Particle Swarm Optimization

Quaternion‐based improved cuckoo algorithm for colour UAV image edge detection

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