Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7380103 | Physica A: Statistical Mechanics and its Applications | 2014 | 11 Pages |
Abstract
In classical Laplacian image sharpening, all pixels are processed one by one, which leads to large amount of computation. Traditional Laplacian sharpening processed on CPU is considerably time-consuming especially for those large pictures. In this paper, we propose a parallel implementation of Laplacian sharpening based on Compute Unified Device Architecture (CUDA), which is a computing platform of Graphic Processing Units (GPU), and analyze the impact of picture size on performance and the relationship between the processing time of between data transfer time and parallel computing time. Further, according to different features of different memory, an improved scheme of our method is developed, which exploits shared memory in GPU instead of global memory and further increases the efficiency. Experimental results prove that two novel algorithms outperform traditional consequentially method based on OpenCV in the aspect of computing speed.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Mathematical Physics
Authors
Tinghuai Ma, Lu Li, Sai Ji, Xin Wang, Yuan Tian, Abdullah Al-Dhelaan, Mznah Al-Rodhaan,