Two-way partitioning of a recursive Gaussian filter in CUDA

Abstract

Recursive Gaussian filters are more efficient than basic Gaussian filters when its filter window size is large. Since the computation of a point should start after the computation of its neighborhood points, recursive Gaussian filters are line oriented. Thus, the degree of parallelism is restricted by the length of the data image. In order to increase the parallelism of recursive Gaussian filters, we propose a two-way partitioned recursive Gaussian filter. The proposed filter partitions a line into two lines and a point, which is used for Gaussian blur effect across the two lines. This partition increases the parallelism because the filter is applied to the two blocks in parallel. Experimental results show that the process time of the proposed filter is half compared to the time of an one-way parallel recursive Gaussian filter while the peak signal-to-noise ratio is maintained within an acceptable rate of 26 to 33 dB.