Wavelet-based image denoising is an important technique in the area of image noise reduction. Wavelets have their natural ability to represent images in a very sparse form which is the foundation of wavelet-based denoising through thresholding. This paper explores properties of several representative thresholding techniques in wavelets denoising, such as VisuShrink, SureShrink, BayesShrink and Feature Adaptive Shrinkage. A quantitative comparison between these techniques through PSNR (Peak Signal-to-Noise Ratio) is also given. © 2011 Published by Elsevier Ltd.
Xiao, F., & Zhang, Y. (2011). A comparative study on thresholding methods in wavelet-based image denoising. In Procedia Engineering (Vol. 15, pp. 3998–4003). https://doi.org/10.1016/j.proeng.2011.08.749