Parallel implementation of a unified approach to image focus and defocus analysis on the Parallel Virtual Machine

A uniied approach to image focus and defocus analysis (UFDA) was proposed recently for three-dimensional shape and focused image recovery of objects. One version of this approach which yields very accurate results is highly computationally intensive. In this paper we present a parallel implementation of this version of UFDA on the Parallel Virtual Machine (PVM). One of the most computationally intensive part of the UFDA approach is the estimation of image data that would be recorded by a camera for a given solution for 3D shape and focused image. This computational step has to be repeated once during each iteration of the optimization algorithm. Therefore this step has been sped up by using the Parallel Virtual Machine (PVM). PVM is a software package that allows a heterogeneous network of parallel and serial computers to appear as a single concurrent computational resource. In our experimental environment PVM is installed on two UNIX workstations communicating over Ethernet to exploit parallel processing capability. Experimental results showed that the communication overhead in this case was comparatively low. An average 1.93 speedup is gained by the parallel UFDA algorithm running on PVM platform compared to the execution time of sequential processing. This illustrates a practical application of PVM to 3D machine vision.