Fast implementation of Gaussian filter by parallel processing of binominal filter
Takahiro Yano, Yoshimitsu Kuroki · 2016
Some of image processing techniques including noise reduction and feature extraction are realized by convolving filters designed for various purposes. A Gaussian filter is a smoothing filter, and is used in various applications such as feature point extraction. However, since coefficients of a Gaussian filter are real numbers, which requires a computational burden especially for large deviation filters. This paper describes an approximation of Gaussian filters using multi-layer convolutions of the basic binomial filter, which is implemented only by an addition and a shift operation. This study also aims at fast implementation with a parallel computing of the binomial filters on GPU (Graphical Processing Units) under CUDA (Compute Unified Device Architecture) platform introduced by NVIDIA corporation.