Open Access

ARTICLE

Medical Image Demosaicing Based Design of Newton Gregory Interpolation Algorithm

by E. P. Kannan^1,*, S. S. Vinsley², T. V. Chithra³

1 Department of ECE, Arunachala College of Engineering for Women, Nagercoil, 629203, India
2 Department of ECE, Lourdes Mount College of Engineering and Technology, Marthandam, 629195, India
3 Department of CSE, Arunachala College of Engineering for Women, Nagercoil, 629203, India

* Corresponding Author: E. P. Kannan. Email:

Intelligent Automation & Soft Computing 2022, 34(3), 1675-1691. https://doi.org/10.32604/iasc.2022.022707

Received 16 August 2021; Accepted 13 December 2021; Issue published 25 May 2022

Abstract

In this paper, Field-Programmable Gate Array (FPGA) implementation-based image demosaicing is carried out. The Newton Gregory interpolation algorithm is designed based on FPGA frame work. Interpolation is the method of assessing the value of a function for any in-between value of self-regulating variable, whereas the method of computing the value of the function outside the specified range is named extrapolation. The natural images are collected from Kodak image database and medical images are collected from UPOL (University of Phoenix Online) database. The proposed algorithm is executed on using Xilinx ISE (Integrated Synthesis Environment) Design Suite 14.2 and is confirmed on Xilinx Virtex-5 FPGA board. The main aim of this paper is to develop a demosaicing method based on the FPGA technique. Implementing image processing on hardware reduces the cost and make simpler for debugging and verification. The hardware implementation in this system gives the better quality of output image. Implementing image processing on hardware reduces the cost and makes simpler for debugging and verification. Newton- Gregory interpolation technique produces the full resolution of green channel with red and blue channels. The experimental result clearly shows that proposed Newton-Gregory interpolation-based image demosaicing outperformed other algorithms. FPGA implementation attains the maximum Peak Signal-To-Noise Ratio (PSNR) of 40.53 dB and MATLAB (Matrix Laboratory). implementation attains the maximum PSNR of 38.15 dB.

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Keywords

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