Open Access

ARTICLE

Defocus Blur Segmentation Using Local Binary Patterns with Adaptive Threshold

Usman Ali, Muhammad Tariq Mahmood^*

Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600, Chungjeol-ro, Byeongcheon-myeon, Cheonan, 31253, Korea

* Corresponding Author: Muhammad Tariq Mahmood. Email:

(This article belongs to this Special Issue: Machine Learning Empowered Secure Computing for Intelligent Systems)

Computers, Materials & Continua 2022, 71(1), 1597-1611. https://doi.org/10.32604/cmc.2022.022219

Received 31 July 2021; Accepted 15 September 2021; Issue published 03 November 2021

Abstract

Enormous methods have been proposed for the detection and segmentation of blur and non-blur regions of the images. Due to the limited available information about blur type, scenario and the level of blurriness, detection and segmentation is a challenging task. Hence, the performance of the blur measure operator is an essential factor and needs improvement to attain perfection. In this paper, we propose an effective blur measure based on local binary pattern (LBP) with adaptive threshold for blur detection. The sharpness metric developed based on LBP used a fixed threshold irrespective of the type and level of blur, that may not be suitable for images with variations in imaging conditions, blur amount and type. Contrarily, the proposed measure uses an adaptive threshold for each input image based on the image and blur properties to generate improved sharpness metric. The adaptive threshold is computed based on the model learned through support vector machine (SVM). The performance of the proposed method is evaluated using two different datasets and is compared with five state-of-the-art methods. Comparative analysis reveals that the proposed method performs significantly better qualitatively and quantitatively against all of the compared methods.

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Keywords

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