压缩感知苹果图像并行快速重构方法研究

Parallel and Fast Reconstruction Algorithm for Compressed Sensing Apple Image

针对压缩感知重构算法耗时长而影响苹果图像快速获取这一问题,分析了二维正交匹配跟踪重构算法的并行性,借助GPU通用并行计算平台,利用CUDA技术,设计其对应的并行化重构算法,从而得到快速的苹果图像重构方法。实验结果表明,并行化的算法可将苹果图像重构效率提高16~35倍,能在数秒内恢复原始图像,为压缩感知应用到果园远程实时监控及基于图像的苹果质量快速检测与分类等场合提供条件。

With the emerging of compressed sensing （CS）, it is possible to overcome the storage and transmission difficulty of the mass data sampled by traditional methods. It also provides a new way for machine vision applied to apple image sampling. However, the major shortcoming of the reconstruction algorithms for CS signals is the expensive computing time, which limits its applications to the occasions requiring fast processing. Aiming at this problem, two dimensional orthogonal matching pursuit algorithm with parallel computing is proposed for apple image reconstruction. The parallelism of the algorithm is analyzed and the parallel algorithm using CUDA technology on GPU is designed in order to achieve a fast reconstruction algorithm. Experimental results show that the parallel algorithm improves the recovery efficiency by 16 to 35 times and the apple image can be recovered in several seconds. This method provides a new technical support to remote monitoring in real time for apple garden. It can be used in the fast apple quality detection based on image as well.