Filtering the unwanted parameters will guarantee the quality of images for further operations. Sensors and other devices used to capture the images may be subjected to situations where it might not function as programmed. The medium in which the images are transformed may also face
the same difficulties. In such cases, salt and pepper noise should be removed before the images are taken into considerations. Otherwise, processes like edge detection, image segmentation and object recognition will be facing hurdles and may not reveal the desired output. This proposal intends
to solve such an issue by using an algorithm for median filtering for using filters with medians on highly affected images. The previous techniques used for filtering out noise are Standard Median Filtering (SMF), Adaptive Median Filtering (AMF) and they have shown relatively lesser performance
than the proposed approach. Implementation includes a FPGA set up and strives to remove impulse noise to a great extent consuming lesser computation time. The quality of output's visual and quantitative metrics has outperformed the discussed previous models.
Real processing components along with component simulators are combined together to construct a new virtual prototyping system. The increase in component simulators result in degraded performance of the simulation in distributed systems. The speed of simulation can be increased by doing
parallel simulation techniques. Prime number test and Image edge detection are chosen to implement the parallel simulation techniques and achieved the expected results while implementing in real time applications. The prime number test calculates the number of processors in a system and the
image edge detection can be done in two stages by Canny Edge detection and Sobel Edge detection. The Canny Edge detection is used to detect the edges in the images by using a multi-stage algorithm. The smaller, separable and integer valued filter in images are combined in horizontal and vertical
directions by using the Sobel edge detection resulting in reduction of implementation cost. The tool named OpenMP is used for implementing the parallel simulation techniques by combining both the canny edge and Sobel edge detection. An add-on named MPI is used along with the OpenMP to reduce
the implementation time in parallel processing.
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