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Effective Blind Image Deblurring Using Matrix-Variable Optimization
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DOI：10.1109/TIP.2021.3073856.png)
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摘要
En 中文
Blind image deblurring has been a challenging issue due to the unknown blur and computation problem. Recently, the matrix-variable optimization method successfully demonstrates its potential advantages in computation. This paper proposes an effective matrix-variable optimization method for blind image deblurring. Blur kernel matrix is exactly decomposed by a direct SVD technique. The blur kernel and original image are well estimated by minimizing a matrix-variable optimization problem with blur kernel constraints. A matrix-type alternative iterative algorithm is proposed to solve the matrix-variable optimization problem. Finally, experimental results show that the proposed blind image deblurring method is much superior to the state-of-the-art blind image deblurring algorithms in terms of image quality and computation time.
Keyword:
Kernel
Matrix decomposition
Image restoration
Matrix converters
Complexity theory
Computational modeling
Optimization methods
Blind image deblurring
kernel decomposition
matrix-variable optimization
matrix-type alternative iteration
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期刊
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13.7
论文数:
1.1W
被引数:
8.8W
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