Title: An adaptive denoising method for colour images of mobile phone based on bivariate shrinkage function

Authors: Xuehui Wu; Xiaobo Lu; Xue Han; Chunxue Liu

Addresses: School of Automation, Southeast University, China; Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, 210096, China ' School of Automation, Southeast University, China; Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, 210096, China ' School of Automation, Southeast University, China; Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, 210096, China ' School of Automation, Southeast University, China; Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing, 210096, China

Abstract: The photography function of mobile phones has become the current trend, and the requirements for image denoising performance are higher and higher. It is different from traditional image denoising because mobile phone image denoising needs faster speed, less computing time and higher efficiency. Wavelet transform denoising algorithm can meet the denoising demand of mobile phone images because of its rapidity, validity and so on. However, denoising by wavelet transform is optimal for Gaussian noise, and the actual noise of images taken by mobile phones does not completely conform to Gaussian distribution, and then down-sampling method is adopted to simulate the Gaussian noise. In this article, an adaptive denoising method for colour images of mobile phone based on bivariate shrinkage function is proposed. Simulation and actual experimental results showed that the method can get better denoising effect compared with other methods.

Keywords: mobile phone image; denoising; down sampling; bivariate shrinkage function; noise variance; greying.

DOI: 10.1504/IJES.2018.095749

International Journal of Embedded Systems, 2018 Vol.10 No.6, pp.484 - 493

Received: 13 Jan 2016
Accepted: 10 Jul 2016
Published online: 22 Oct 2018 *

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