Open Access

ARTICLE

Contrast Correction Using Hybrid Statistical Enhancement on Weld Defect Images

Wan Azani Mustafa^1,2,*, Haniza Yazid³, Ahmed Alkhayyat⁴, Mohd Aminudin Jamlos³, Hasliza A. Rahim³, Midhat Nabil Salimi⁵

1 Advanced Computing (AdvCOMP), Centre of Excellence, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau, 02600, Perlis, Malaysia
2 Faculty of Electrical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau, 02600, Perlis, Malaysia
3 Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Pauh Putra Campus, Arau, 02600, Perlis, Malaysia
4 Faculty of Engineering, the Islamic University, 54001, Najaf, Iraq
5 Faculty of Chemical Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau, 02600, Perlis, Malaysia

* Corresponding Author: Wan Azani Mustafa. Email:

Computers, Materials & Continua 2022, 71(3), 5327-5342. https://doi.org/10.32604/cmc.2022.023492

Received 10 September 2021; Accepted 22 October 2021; Issue published 14 January 2022

Abstract

Luminosity and contrast variation problems are among the most challenging tasks in the image processing field, significantly improving image quality. Enhancement is implemented by adjusting the dark or bright intensity to improve the quality of the images and increase the segmentation performance. Recently, numerous methods had been proposed to normalise the luminosity and contrast variation. A new approach based on a direct technique using statistical data known as Hybrid Statistical Enhancement (HSE) is presented in this study. The HSE method uses the mean and standard deviation of a local and global neighbourhood and classified the pixel into three groups; the foreground, border, and problematic region (contrast & luminosity). The datasets, namely weld defect images, were utilised to demonstrate the effectiveness of the HSE method. The results from the visual and objective aspects showed that the HSE method could normalise the luminosity and enhance the contrast variation problem effectively. The proposed method was compared to the two (2) populor enhancement methods which is Homomorphic Filter (HF) and Difference of Gaussian (DoG). To prove the HSE effectiveness, a few image quality assessments were presented, and the results were discussed. The HSE method achieved a better result compared to the other methods, which are Signal Noise Ratio (8.920), Standard Deviation (18.588) and Absolute Mean Brightness Error (9.356). In conclusion, implementing the HSE method has produced an effective and efficient result for background correction and quality images improvement.

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Keywords

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