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Parallel optimization exploration of massive image fast edge extraction in chip multi-processor environment

Edge Extraction is a practical application that uses image sharpening technology to detect the edge of objects in an image, which has been widely used in Image Processing area. As the volume of an image increases large enough, meanwhile the cost time requires short enough, there comes a new research problem, i.e., massive image fast edge extraction. This paper focuses on the abovementioned problem, explores and proposes a set of optimization solutions with the idea of parallel computing. Firstly, two parallel computing models, Shared-Memory Model and Message Passing Model and their implementations are introduced, and the time estimation equations are built to analyze the parallel performance. Secondly, a sequential edge extraction algorithm based on Sobel Operator is parallelized based the two parallel computing models, and the factors that impact the parallel performance are analyzed. Thirdly, a set of comparison experiments are conducted in Chip Multi-Processor environment, and the conclusion is summarized to give future research inspiration.