Abstract
The digitization of information is abundant in different areas related to digital image processing; its primary objective is to improve the quality of the image for a correct human interpretation or to facilitate the search of information patterns in a shorter time, with fewer computing resources, size and low energy consumption. This research is focused on validating a possible implementation using a limited embedded system, so the specified processing speed and algorithms that redistribute the computational cost are required. The strategy has been based on parallel processing for the distribution of tasks and data to the Epiphany III. It was combined to reduce the factors that introduce noise to the image and improve quality. The most common types of noise are Gaussian noise, impulsive noise, uniform noise and speckle noise. In this paper, the effects of Gaussian noise that occurs at the moment of the acquisition of the image that produces as a consequence blur in some pixels of the image is analyzed, and that generates the effect of haze (blur). The implementation was developed using the Many-core technology in 2 × 2 and 4 × 4 arrays with (4, 8, 16) cores, also the performance of the Epiphany system was characterized to FFT2D, FFT setup, BITREV, FFT1D, Corner turn and LPF and the response times in machine cycles of each algorithm are shown. The power of parallel processing with this technology is displayed, and the low power consumption is related to the number of cores used. The contribution of this research in a qualitative way is demonstrated with a slight variation for the human eye in each other images tested, and finally, the method is a useful tool for applications with resources limited.
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Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Education, London (2008)
Camarena: Progress in Pattern Recognition, Image Analysis. Computer Vision (2009)
Grama, A., Gupta, A., Karypis, G., Kumar, V.: An Introduction to Parallel Computing Design and Analysis of Algorithms, 2nd edn. Pearson Addison Wesley, Boston (2003)
Burger, T.: Intel Multi-Core Processors. Quick Reference Guide. http://cachewww.intel.com/cd/00/00/23/19/231912_231912.pdf
Wentzlaff, D., et al.: On-chip interconnection architecture of the tile processor. IEEE Micro 27, 15–31 (2007)
Kalray’s MPPA: User’s Guide. http://www.kalray.eu/products/mppa-many-core/mppa-256/
Adapteva: The Parallella Board. User’s Guide. http://www.adapteva.com/parallella-board/
INTEL: Intel Xeon Phi Coprocessor. User’s Guide. http://www.ssl.intel.com/content/dam/www/public/us/en/documents/datasheets/xeonphi-coprocessor-datasheet.pdf
INTEL: The Single-Chip-Cloud Computer. User’s Guide. http://www.intel.com/content/dam/www/public/us/en/documents/technologybriefs/intel-labs-single-chip-cloud-rticle.pdf
Benini, L., Micheli, G.: Networks on chips: a new SoC paradigm. Computer 35, 70–78 (2002)
Scherson, I.D., Youssef, A.S.: Interconnection Networks for High-Performance Parallel Computers. IEEE Computer Society Press (1994)
Teodoro, A.S., Miguel, A.M.R.: Factors influencing many-core processor. cicomp (2014)
Wittwer, T.: An Introduction to Parallel Programming, 1st edn. VSSD, Leeghwaterstraat 42, 2628 CA Delft, The Netherlands (2006)
Dongarra, J., et al.: Sourcebook of Parallel Computing. Morgan Kaufmann Publishers, Burlington (2003)
Hwang, K., Xu, Z.: Scalable Parallel Computing. McGraw Hill, New York (1998)
Yaniv, S.: Scalable Parallel Multiplication of Big Matrices, 6 February 2014. http://www.adapteva.com/white-papers/scalable-parallel-multiplication-of-big-matrices/
Cannon, L.E.: A cellular computer to implement the Kalman filter algorithm. DTIC Document. Technical report (1969)
Adapteva: Epiphany Technical Reference Documents. User’s Guide, 6 February 2014. http://www.adapteva.com/all-documents
Camarena, J.G., Gregori, V., Morillas, S., Sapena, A.: Fast detection and removal of impulsive noise using peer groups and fuzzy metrics. J. Vis. Commun. Image Represent. 19(1), 20–29 (2008)
Plataniotis, K.N., Venetsanopoulos, A.N.: Color Image Processing and Applications (2000)
Vajda, A.: Programming Many-Core Chips. Springer, Heidelberg (2011). https://doi.org/10.1007/978-1-4419-9739-5
Diaz, J., Muñoz-Caro, C., Nino, A.: A survey of parallel programming models and tools in the multi and many-core era. IEEE Trans. Parallel Distrib. Syst. 23(8), 1369–1386 (2012)
Acknowledgement
We appreciate the facilities granted for the completion of this work to the Instituto Politécnico Nacional through the Secretaria de Investigación y posgrado (SIP) with the SIP 20180023, 20180824 project, Unidad Interdisciplinaria de Ingeniería y Ciencias Sociales y Administrativas y Centro de Investigación y Desarrollo de Tecnología Digital. Likewise, to the Program of Stimulus to the Performance of the Researchers and the Program of Stimulus to the Teaching Performance (EDD) and COFAA.
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Alvarez-Sanchez, T., Alvarez-Cedillo, J.A., Sandoval-Gutierrez, J. (2019). Many-Core Parallel Algorithm to Correct the Gaussian Noise of an Image. In: Torres, M., Klapp, J., Gitler, I., Tchernykh, A. (eds) Supercomputing. ISUM 2018. Communications in Computer and Information Science, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-10448-1_7
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DOI: https://doi.org/10.1007/978-3-030-10448-1_7
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