Lawrence Berkeley National Laboratory

Image smoothing is a fundamental operation in computer vision and image processing. This work has two main thrusts: (1) implementation of a bilateral filter suitable for use in smoothing, or denoising, 3D volumetric data; (2) implementation of the 3D bilateral filter in three different parallelization models, along with parallel performance studies on two modern HPC architectures. Our bilateral filter formulation is based upon the work of Tomasi [11], but extended to 3D for use on volumetric data. Our three parallel implementations use POSIX threads, the Message Passing Interface (MPI), and Unified Parallel C (UPC), a Partitioned Global Address Space (PGAS) language. Our parallel performance studies, which were conducted on a Cray XT4 supercomputer and a quad-socket, quad-core Opteron workstation, show our algorithm to have near-perfect scalability up to 120 processors. Parallel algorithms, such as the one we present here, will have an increasingly important role for use in production visual analysis systems as the underlying computational platforms transition from single- to multi-core architectures in the future.