Huang Liqing, Xia Youshen, Ye Tiantian
IEEE Trans Image Process. 2021;30:4653-4666. doi: 10.1109/TIP.2021.3073856. Epub 2021 May 3.
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.
由于模糊情况未知以及计算问题，盲图像去模糊一直是一个具有挑战性的问题。最近，矩阵变量优化方法成功地展示了其在计算方面的潜在优势。本文提出了一种用于盲图像去模糊的有效矩阵变量优化方法。通过直接奇异值分解（SVD）技术精确分解模糊核矩阵。通过最小化具有模糊核约束的矩阵变量优化问题，对模糊核和原始图像进行了良好估计。提出了一种矩阵型交替迭代算法来解决矩阵变量优化问题。最后，实验结果表明，所提出的盲图像去模糊方法在图像质量和计算时间方面明显优于现有最先进的盲图像去模糊算法。
