Parallelization and Automation of a Blind Deconvolution Algorithm

Multiple image processing algorithms are often required to process computer vision inputs. The rapid processing of complex image streams requires more computing power than is found in a typical PC based computer or workstation, and the processing power of high-performance computers (HPCs) and Linux clusters have been required to do this type of rapid massive processing. Emerging multicore processors offer the possibility of doing these types of processing at the PC level in real time.The Physically Constrained Iterative Deconvolution (PCID) algorithm is a multi-frame blind deconvolution (MFBD) parallel algorithm that allows the extraction of simple and complex information from multiple images. Massive computing power is required to use this algorithm in real time. Message Passing Interface (MPI) is normally used with PCID for communications between processors in multiprocessor systems. However, MPI has fault tolerant issues. A tool to replace MPI for multiprocessor communications has been developed that supports a high degree of fault-tolerance, and facilitates multiple image processing by integration with a publication/subscription infrastructure. This tool is demonstrated here for the PCID algorithm. Other attributes of MPI and this tool's publication/subscription information management support for PCID are compared and contrasted.

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“…The algorithm was initially parallelized using MPI, which resulted in improved performance [5]. The PCID/MPI code was then rewritten to use PSQ.…”

Section: Pcid Implementationmentioning

confidence: 99%

“…This paper discusses the use of the JBI to support the Physically Constrained Iterative Deconvolution (PCID) algorithm [5], a multi-frame blind deconvolution algorithm for image deblurring.…”

Section: Introductionmentioning

confidence: 99%