Optimizing Gaussian filtering of volumetric data using SSE

Gaussian filtering is a basic operation commonly used in numerous image and volume processing algorithms. It is, therefore, desirable to perform it as efficiently as possible. Over the last decade CPUs have been successfully extended with several SIMD (Single Instruction Multiple Data) extensions, such as MMX, 3DNow!, and SSE series. In this paper we introduce a new technique for Gaussian filtering of volume data sets—the extended volume—together with its SIMD implementation using the SSE technology. We further introduce a SIMD optimized recursive IIR implementation of the Gaussian filter, and finally, we parallelize the SSE versions with the help of OpenMP (Open Multi-Processing). Experimental evaluation indicates that the SIMD implementation can significantly speed up both versions of the Gaussian filtering and that the non-recursive extended volume version is faster than the recursive IIR one for small widths of the Gaussian filter. Copyright 2010 John Wiley & Sons, Ltd.