13 May 2017 Adaptive denoising for simplified signal-dependent random noise model in optoelectronic detector
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Abstract
Existing denoising algorithms based on a simplified signal-dependent noise model are valid under the assumption of the predefined parameters. Consequently, these methods fail if the predefined conditions are not satisfied. An adaptive method for eliminating random noise from the simplified signal-dependent noise model is presented in this paper. A linear mapping function between multiplicative noise and noiseless image data is established using the Maclaurin formula. Through demonstrations of the cross-correlation between random variables and independent random variable functions, the mapping function between the variances of multiplicative noise and noiseless image data is acquired. Accordingly, the adaptive denoising model of simplified signal-dependent noise in the wavelet domain is built. The experimental results confirm that the proposed method outperforms conventional ones.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Yu Zhang, Weiping Wang, Guangyi Wang, Jiangtao Xu, "Adaptive denoising for simplified signal-dependent random noise model in optoelectronic detector," Optical Engineering 56(5), 053105 (13 May 2017). https://doi.org/10.1117/1.OE.56.5.053105 . Submission: Received: 9 January 2017; Accepted: 27 April 2017
Received: 9 January 2017; Accepted: 27 April 2017; Published: 13 May 2017
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