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Edge detection on noisy images using Prewitt operator and fractional order differentiation

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Abstract

Edge detection is the most important step in finding discontinuities and exploring boundaries on digital images. This paper presents a novel method for edge detection using fractional order differentiation (FOD) coupled with Prewitt operator. FOD employs information of neighboring pixels to perform weighted averaging implicitly to not only calculate derivative of the image but also eliminate noise. Performing various experiments on sample images, visual evaluation of the results indicated superiority of the proposed method over five traditional and six recently proposed edge detection methods. Finally, performance evaluation of Prewitt fractional order edge detection (FOED) based on Pratt’s figure of merit (FOM) showed its promising potentials for edge detection on medical images.

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Authors and Affiliations

  1. Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Khorasan-e-Razavi, 9691664791, Iran

    Saeed Balochian

  2. Ferdows Branch, Islamic Azad University, Ferdows, Iran

    Hossein Baloochian

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  1. Saeed Balochian
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  2. Hossein Baloochian
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Correspondence to Saeed Balochian.

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Balochian, S., Baloochian, H. Edge detection on noisy images using Prewitt operator and fractional order differentiation. Multimed Tools Appl 81, 9759–9770 (2022). https://doi.org/10.1007/s11042-022-12011-1

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