基于GPU图像去噪总变分对偶模型的并行计算

研究基于总变分(TV)的图像去噪问题,针对中央处理器(CPU)计算速度较慢的问题,提出了在图像处理器(GPU)上并行计算的方法。考虑总变分最小问题的对偶模型,建立原始变量与对偶变量的关系,采用梯度投影算法求解对偶变量。数值实验分别在GPU与CPU上进行。实验结果表明,总变分去噪模型对偶算法在GPU设备上执行的效率高于在CPU上执行的效率,并且随着图像尺寸的增大,GPU并行计算的优势更加突出。

Parallel computation for image denoising via total variation dual model on GPU

The problem of Total Variation (TV)-based image denoising was considered. Since the traditional serial computation speed based on Central Processing Unit (CPU) was low, a parallel computation based on Graphics Processing Unit (GPU) was proposed. The dual model of the total variation-based image denoising was derived and the relationship between the primal variable and the dual variable was considered. The projected gradient method was applied to solve the dual model. Numerical results obtained by CPU and GPU show that the algorithm implemented by GPU is more efficient than that by CPU, and with the increasing of image size, the advantage of GPU parallel computing is more outstanding.