Vol.70, No.3, 2022, pp.4867-4882, doi:10.32604/cmc.2022.019544
OPEN ACCESS
ARTICLE
Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold
  • Muhammad Tariq Mahmood*
Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, Cheonan, 31253, Byeongcheon-myeon, Korea
* Corresponding Author: Muhammad Tariq Mahmood. Email:
(This article belongs to this Special Issue: Security and Privacy issues for various Emerging Technologies and Future Trends)
Received 16 April 2021; Accepted 03 June 2021; Issue published 11 October 2021
Abstract
Detection and classification of the blurred and the non-blurred regions in images is a challenging task due to the limited available information about blur type, scenarios and level of blurriness. In this paper, we propose an effective method for blur detection and segmentation based on transfer learning concept. The proposed method consists of two separate steps. In the first step, genetic programming (GP) model is developed that quantify the amount of blur for each pixel in the image. The GP model method uses the multi-resolution features of the image and it provides an improved blur map. In the second phase, the blur map is segmented into blurred and non-blurred regions by using an adaptive threshold. A model based on support vector machine (SVM) is developed to compute adaptive threshold for the input blur map. The performance of the proposed method is evaluated using two different datasets and compared with various state-of-the-art methods. The comparative analysis reveals that the proposed method performs better against the state-of-the-art techniques.
Keywords
Blur measure; blur segmentation; sharpness measure; genetic programming; support vector machine
Cite This Article
Mahmood, M. T. (2022). Defocus Blur Segmentation Using Genetic Programming and Adaptive Threshold. CMC-Computers, Materials & Continua, 70(3), 4867–4882.
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