A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images
Defocus blur is often encountered in images taken with optical imaging equipment. It might be unwanted, but it might also be a deliberate artistic effect, which means it might help how we see the scenario in an image. In specific applications like image restoration or object detection, there may be...
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MDPI AG
2025-01-01
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| author | Sana Munir Khan Muhammad Tariq Mahmood |
| author_facet | Sana Munir Khan Muhammad Tariq Mahmood |
| author_sort | Sana Munir Khan |
| collection | DOAJ |
| description | Defocus blur is often encountered in images taken with optical imaging equipment. It might be unwanted, but it might also be a deliberate artistic effect, which means it might help how we see the scenario in an image. In specific applications like image restoration or object detection, there may be a need to divide a partially blurred image into its blurred and sharp regions. The effectiveness of blur detection is influenced by how features are combined. In this paper, we propose a parameter-free metaheuristic optimization strategy known as teacher-learning-based optimization (TLBO) to find an optimal weight vector for the combination of blur maps. First, we compute multi-scale blur maps, i.e., features using an LBP-based blur metric. Then, we apply a regularization scheme to refine the initial blur maps. This results in a smooth, edge-preserving blur map that leverages structural information for improved segmentation. Lastly, TLBO is used to find the optimal weight vectors of each refined blur map for the linear feature combination. The proposed model is validated through extensive experiments on two benchmark datasets, and its performance is comparable against five state-of-the-art methods. |
| format | Article |
| id | doaj-art-9fdfc326c0fe41c6b146dcaf038f964e |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
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| series | Mathematics |
| spelling | doaj-art-9fdfc326c0fe41c6b146dcaf038f964e2025-01-24T13:39:40ZengMDPI AGMathematics2227-73902025-01-0113218710.3390/math13020187A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused ImagesSana Munir Khan0Muhammad Tariq Mahmood1Future Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of KoreaFuture Convergence Engineering, School of Computer Science and Engineering, Korea University of Technology and Education, 1600 Chungjeolro, Byeongcheonmyeon, Cheonan 31253, Republic of KoreaDefocus blur is often encountered in images taken with optical imaging equipment. It might be unwanted, but it might also be a deliberate artistic effect, which means it might help how we see the scenario in an image. In specific applications like image restoration or object detection, there may be a need to divide a partially blurred image into its blurred and sharp regions. The effectiveness of blur detection is influenced by how features are combined. In this paper, we propose a parameter-free metaheuristic optimization strategy known as teacher-learning-based optimization (TLBO) to find an optimal weight vector for the combination of blur maps. First, we compute multi-scale blur maps, i.e., features using an LBP-based blur metric. Then, we apply a regularization scheme to refine the initial blur maps. This results in a smooth, edge-preserving blur map that leverages structural information for improved segmentation. Lastly, TLBO is used to find the optimal weight vectors of each refined blur map for the linear feature combination. The proposed model is validated through extensive experiments on two benchmark datasets, and its performance is comparable against five state-of-the-art methods.https://www.mdpi.com/2227-7390/13/2/187defocus blurobject detectionmulti-scalemetaheuristic optimizationparameter-free |
| spellingShingle | Sana Munir Khan Muhammad Tariq Mahmood A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images Mathematics defocus blur object detection multi-scale metaheuristic optimization parameter-free |
| title | A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images |
| title_full | A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images |
| title_fullStr | A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images |
| title_full_unstemmed | A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images |
| title_short | A Teacher-Learning-Based Optimization Approach for Blur Detection in Defocused Images |
| title_sort | teacher learning based optimization approach for blur detection in defocused images |
| topic | defocus blur object detection multi-scale metaheuristic optimization parameter-free |
| url | https://www.mdpi.com/2227-7390/13/2/187 |
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