Affiliations: [a] School of Computer Science and Engineering, University of Electronic Science and Technology of China, China
| [b] Department of Mathematics and Statistics, Concordia University, de Maisonneuve West, Montreal, Quebec, Canada
Correspondence:
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Corresponding author. Guangyi Chen, Department of Mathematics and Statistics, Concordia University, 1455 de Maisonneuve West, Montreal, Quebec H3G 1M8, Canada. Tel.: +1 514 2715089; Fax: +1 514 8482830; E-mail: [email protected].
Abstract: Noise reduction is a very important topic in image processing. In this paper, we present a novel method for reducing noise in an image corrupted by a mixture of Gaussian white noise and signal dependent noise. Our method can be built from any existing denoising methods. The main steps of our method can be described as follows: (a) reduce noise from the input noisy image, (b) take the logarithm of the denoised image, (c) reduce noise from the logarithm image, and (d) transform this noise-reduced logarithm image back to the original space. We conduct experiments for seven gray scale images and we find that our method is always better than the method that it was built up from in term of peak signal to noise ratio (PSNR). However, our method is comparable to total least square (TLS) method, which is specifically designed for reducing signal dependent noise. The PSNR’s of our method are sometimes higher and sometimes lower than those of the TLS method. Nevertheless, our method is much faster than the TLS method in CPU computation time.
Keywords: Image denoising, signal-dependent noise, bivariate wavelet shrinkage (BivShrink), non-local means (NL-Means), block matching 3D filtering (BM3D)
