Article citationsMore>>

J. Portilla and E. P. Simoncelli, “Adaptive Wiener Denoising Using a Gaussian Scale Mixture Model in the Wavelet Domain,” IEEE International Conference on Image Process (ICIP), Vol. 2, 2001, pp. 37-40.

has been cited by the following article:

• TITLE: Denoising of an Image Using Discrete Stationary Wavelet Transform and Various Thresholding Techniques

  AUTHORS: Abdullah Al Jumah

  KEYWORDS: Wavelet; Discrete Wavelet Transform; Wavelet Packet Transform; Stationary Wavelet Transform; Thresholding; Visu Shrink; SURE Shrink; Normal Shrink; Mean Square Error; Peak Signal-to-Noise Ratio

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.4 No.1, February 27, 2013

  ABSTRACT: Image denoising has remained a fundamental problem in the field of image processing. With Wavelet transforms, various algorithms for denoising in wavelet domain were introduced. Wavelets gave a superior performance in image denoising due to its properties such as multi-resolution. The problem of estimating an image that is corrupted by Additive White Gaussian Noise has been of interest for practical and theoretical reasons. Non-linear methods especially those based on wavelets have become popular due to its advantages over linear methods. Here I applied non-linear thresholding techniques in wavelet domain such as hard and soft thresholding, wavelet shrinkages such as Visu-shrink (non-adaptive) and SURE, Bayes and Normal Shrink (adaptive), using Discrete Stationary Wavelet Transform (DSWT) for different wavelets, at different levels, to denoise an image and determine the best one out of them. Performance of denoising algorithm is measured using quantitative performance measures such as Signal-to-Noise Ratio (SNR) and Mean Square Error (MSE) for various thresholding techniques.