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FPGA-Based Re-Configurable Architecture for Window-Based Image Processing

FPGA-Based Re-Configurable Architecture for Window-Based Image Processing

Kamarujjaman Sk, Manali Mukherjee, Mausumi Maitra
Copyright: © 2017 |Pages: 46
ISBN13: 9781522508892|ISBN10: 1522508899|EISBN13: 9781522508908
DOI: 10.4018/978-1-5225-0889-2.ch001
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MLA

Sk, Kamarujjaman, et al. "FPGA-Based Re-Configurable Architecture for Window-Based Image Processing." Multi-Core Computer Vision and Image Processing for Intelligent Applications, edited by Mohan S. and Vani V., IGI Global, 2017, pp. 1-46. https://doi.org/10.4018/978-1-5225-0889-2.ch001

APA

Sk, K., Mukherjee, M., & Maitra, M. (2017). FPGA-Based Re-Configurable Architecture for Window-Based Image Processing. In M. S. & V. V. (Eds.), Multi-Core Computer Vision and Image Processing for Intelligent Applications (pp. 1-46). IGI Global. https://doi.org/10.4018/978-1-5225-0889-2.ch001

Chicago

Sk, Kamarujjaman, Manali Mukherjee, and Mausumi Maitra. "FPGA-Based Re-Configurable Architecture for Window-Based Image Processing." In Multi-Core Computer Vision and Image Processing for Intelligent Applications, edited by Mohan S. and Vani V., 1-46. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-0889-2.ch001

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Abstract

In this proposed book chapter, a simple but efficient presentation of Median Filter, Switching Median Filter, Adaptive Median Filter and Decision-Based Adaptive Filtering Method and their hardware architecture for FPGA is described for removal of up to 99% impulse noise from Digital Images. For hardware architecture, simulation is done using Xilinx ISE 14.5 software of XILINX. For implementation, these approaches utilize Genesys VIRTEX V FPGA device of XC5VLX50T device family. In this approach, we proposed an efficient design for suppression of impulse noise from digital images corrupted by up to 99% impulse noise using decision based adaptive filtering method as well as preserve the details of image. The method works in two different stages – noise detection using switching technique and finally noise suppression and restoration. Experimental results show that our method perform better in terms of PSNR below 80% noise density but above 80% noise density it is almost comparable with the latest methods.

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