Abstract: A sequence of subset conjugate graduate optimization based super-resolution algorithm is proposed for super-resolution, where image registration algorithm is used to estimate the displacement and rotation between different low-resolution images, prior knowledge is used for regulation and building the cost function, and the high-resolution image is estimated by using sequence subset conjugate graduate optimization algorithm. Simulation results demonstrate that the method can realize the super-resolution with 15% memory reduction and 20% high speed promotion.

Key words: image reconstruction, super-resolution, conjugate graduate

摘要： 提出一种基于序列子集共轭梯度最优化算法的超分辨率图像重建算法。通过图像配准算法估计得到不同低分辨率图像间的平移和旋转量，结合期望图像的统计先验对问题进行规整，建立优化的代价函数。利用序列子集共轭梯度最优化迭代算法求解，得到高分辨率图像。仿真结果表明，该算法可以使内存降低15％，运算速度提高20％。

关键词: 图像重构, 超分辨率, 共轭梯度

CLC Number: 

• TP311