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IJSTR >> Volume 10 - Issue 5, May 2021 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Implementing Median Filter On CPU And MIC Using Histogram Approach

[Full Text]

 

AUTHOR(S)

Elmasry, Mohamed Abbas

 

KEYWORDS

Median Filter, CPU, MIC, OpenMP, Histogram, Image Processing, Histogram Approach,

 

ABSTRACT

The Median Filter (MF) is one of the image preprocessing approaches that require considerable computational resources to perform its operation in a moderate time. The MF can be implemented on traditional CPUs and Intel Many Integrated Core Architecture MIC such as Xeon-Phi coprocessors. This paper addresses the use of histogram algorithm to solve the MF on both traditional CPUs and MIC. Different r values and frame sizes are investigated. OpenMP has been deployed on CPUs and MIC. Experimental results show that histogram approach performs better than traditional insertion sort approach. It also shows that the use of both CPU and MIC architectures together can lead to much better results when proper scheduling strategy is used to assign the workloads.

 

REFERENCES

[1] H. M. Faheem and B. König-Ries, “A Multiagent-based Framework for Solving Computationally Intensive Problems on Heterogeneous Architectures,” in Proceedings of the 16th International Conference on Enterprise Information Systems-Volume 1, 2014, pp. 526–533.
[2] D. S. Richards, “VLSI Median Filters,” IEEE Trans. Acoust., 1990.
[3] I. Katib, “Implementing Median Filter on Heterogeneous Architectures,” Int. J. Comput. Appl., 2020.
[4] S. Perreault and P. Hébert, “Median filtering in constant time,” IEEE Trans. Image Process., 2007.
[5] G. Gupta, “Algorithm for Image Processing Using Improved Median Filter and Comparison of Mean, Median and Improved Median Filter,” Int. J. Soft Comput., 2011.
[6] K. Verma, B. Kumar Singh, and A. S. Thokec, “An enhancement in adaptive median filter for edge preservation,” in Procedia Computer Science, 2015.
[7] Y. He, P. Liu, Z. Wang, Z. Hu, and Y. Yang, “Filter pruning via geometric median for deep convolutional neural networks acceleration,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2019.
[8] H. M.Faheem and B. König-Ries, “A New Scheduling Strategy for Solving the Motif Finding Problem on Heterogeneous Architectures,” Int. J. Comput. Appl., 2014.