A GPU Accelerated Local Polynomial Approximation Algorithm for Efficient Denoising of MR Images

被引：1

作者：

Klepaczko, Artur ^{[1
]}

机构：

[1] Lodz Univ Technol, Inst Elect, PL-90924 Lodz, Poland

来源：

PROCEEDINGS OF THE 8TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS CORES 2013 | 2013年 / 226卷

关键词：

NOISE;

D O I：

10.1007/978-3-319-00969-8_41

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a parallelized implementation of the Local Polynomial Approximation algorithm targetted at CUDA-enabled GPU hardware. Although the application area of LPA in the image processing domain is very wide, here the focus is put on magnetic resonance image de-noising. In this case, LPA serves as a pre-processing step in the method based on Shape-Adaptive Discrete Cosine Transform. It is shown, how the designed efficient implementation of LPA substantially reduces the execution time of SA-DCT.

引用

页码：419 / 428

页数：10

共 9 条

[1] Shape-Adaptive DCT for Denoising of 3D Scalar and Tensor Valued Images [J].