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논문 기본 정보

자료유형
학술저널
저자정보
Laya Tojo (The Oxford College of Engineering) Manju Devi (The Oxford College of Engineering) Vivek Maik (SRM Institute of Science and Technology) Gurushankar (South Ural State University)
저널정보
대한전자공학회 IEIE Transactions on Smart Processing & Computing IEIE Transactions on Smart Processing & Computing Vol.12 No.1
발행연도
2023.2
수록면
1 - 8 (8page)
DOI
10.5573/IEIESPC.2023.12.1.1

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초록· 키워드

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In this paper, we propose a blind image deblurring algorithm using block-augmented Lagrangian and low-rank priors (BALLORG) as a non-learning method that can give better results without the complexity of learning-based methods. The proposed algorithm achieves faster convergence within 20 iterations than conventional methods. Regularization priors are used in the form of gradients and sparse low-rank matrices, and recursive rank improvements result in better deblurring performance. The steepest descent in minimization is maintained through weight selection for penalty and regularization parameters. The block processing introduces local and global optimization, leading to better visual quality outputs. The proposed method has excellent performance in terms of the PSNR, SSIM, and FSIM matrix, which is on par with or better than that of other state-of-the-art learning and non-learning-based approaches.

목차

Abstract
1. Introduction
2. Related Work
3. The Proposed Scheme
4. Experimental Results
5. Conclusion
References

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