PIWA-LOC--一种Cluster环境下的大图像并行重采样算法

图像重采样问题应用广泛，具有计算复杂度高、运行时间长的特点．为了提高处理性能，针对Cluster并行环境，对一种并行几何校正算法进行改进，提出了并行重采样算法PIWA—LOC．采用一种新的存储结构用于保存各计算结点上的不规则输出子图像，并提出线段近似法用于获取不规则输出子图像的边界，使算法的通用性大大提高，适用于具有复杂几何变换的图像重采样问题．实验结果表明，该算法对大图像的重采样问题具有良好的并行性能，且网络带宽越高算法的可扩展性越好．

PIWA-LOC-A Parallel Resampling Algorithm for Large Images on Cluster Systems

Image resampling is a computation-intensive task and can be found in many applications To achieve better performance and generalization, a distributed parallel geometric correction algorithm is improved and a parallel resampling algorithm called PIWA-LOC is presented under cluster systems In PIWA-LOC, the input image is partitioned evenly Each computing node calculates the corresponding area in the output space for the local input subimage, and performs resampling for the output pixels in this area A data structure is put forward to save irregular-shaped output subimages, and a piece-wise linear approximation method is explored to get the area of the output subimages, which achieves good generalization for the algorithm Experimental results show that the algorithm is suitable for many complex geometric transformations, and achieves good parallel performance for large image resampling tasks, especially under a cluster system with high network bandwidth