摘要
改进了有参超分辨率图像盲恢复中的边界与正则处理以及模糊参数估计方法,用交替迭代优化来估计模糊参数和高分辨率图像,用Neumann边界图像模型和修改边界像素来避免边界错误,用近似重排系统矩阵的带结构矩阵,来构造求解高分辨率图像的预处理共轭梯度算法。人工退化图像序列上的实验结果表明了改进方法的有效性。
To improve the boundary condition processing, the regularization processing, and the blurring parameter estimation of parametric blind super-resolution image restoration, an alternative iteration optimization is used to estimate the image blurring and high-resolution image. The neumann boundary condition and the boundary pixel modification of the lower-resolution image are adopted to avoid the boundary errors. In solving the high-resolution image, the matrixes close to the reordered system matrix and with special structures are used to make the preconditioner in the preconditioned conjugate gradient algorithm. The test results using the synthetic low-resolution sequences are presented to show the validation of the proposed improvements.
出处
《南京理工大学学报》
EI
CAS
CSCD
北大核心
2006年第3期343-347,共5页
Journal of Nanjing University of Science and Technology
基金
香港特区政府研究资助局资助项目(CUHK/4180/01E)
江苏省教育厅自然科学基金(04KJD520037)
关键词
超分辨率图像盲恢复
交替优化
NEUMANN边界
预处理器
边界修正
blind super-resolution image restoration
alternative optimization
Neumann boundary
preconditioner
boundary modification