Polynomial Differentiation Threshold based Edge Detection of Contrast Enhanced Images

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Author(s)

Kuldip Acharya 1,* Dibyendu Ghoshal 2

1. Department of Computer Science and Engineering, National Institute of Technology, Agartala, Barjala, Jirania, Tripura (W), Pin: 799046, India

2. Department of Electronics and Communication Engineering, National Institute of Technology, Agartala, Barjala, Jirania, Tripura (W), Pin: 799046, India

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2023.02.04

Received: 19 Mar. 2022 / Revised: 30 Apr. 2022 / Accepted: 18 Jun. 2022 / Published: 8 Apr. 2023

Index Terms

Histogram equalization, Harmonic mean, Mean Absolute Deviation, Polynomial differentiation, Thresholding, Edge Detection, Image enhancement

Abstract

This paper uses a two-step method for edge detection using a polynomial differentiation threshold on contrast-enhanced images. In the first step, to enhance the image contrast, the mean absolute deviation and harmonic mean brightness values of the images are calculated. Mean absolute deviation is used to perform the histogram clipping to restrict over-enhancement. First, the clipped histogram is divided in half, and then two sub-images are created and equalized, and combined into a final image that keeps image quality. The second phase involves edge detection using a polynomial differentiation-based threshold on contrast-improved visuals. The polynomial differentiation curve-fitting method was used to smooth the histogram data. The nearest index value to zero is utilized to calculate the threshold value to detect the edges. The significance of the proposed work is to contrast enhancement of low-light images to extract the edge lines. Its value or merit is to achieve improved edge results in terms of various image quality metrics. The findings of the proposed research work are to detect the edges of low-contrast images. Image quality metrics are computed and it is observed that the suggested algorithm surpasses former methods in respect of Edge-based contrast measure (EBCM), Performance Ratio, F-Measure, and Edge-strength similarity-based image quality metric (ESSIM).

Cite This Paper

Kuldip Acharya, Dibyendu Ghoshal, "Polynomial Differentiation Threshold based Edge Detection of Contrast Enhanced Images", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.2, pp. 35-46, 2023. DOI:10.5815/ijigsp.2023.02.04

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