Extensions to MDL denoising
published: Aug. 12, 2008, recorded: July 2008, views: 2999
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
The minimum description length principle in wavelet denoising can be extended from the standard linear-quadratic setting in several ways. We describe briefly three extensions: soft thresholding, histogram modeling and a multicomponent approach. The MDL hard thresholding approach based on the normalized maximum likelihood universal modeling can be extended to include soft thresholding shrinkage, which can be considered to give better results in some applications. In MDL histogram denoising approach the assumptions of the parametric density models for the data can be relaxed. The informative and noise components of the data are modeled with equal bin width histograms. The method can cope with different noise distributions. In multicomponent approach more than one non-noise components are included in the model, because it is possible that in addition to the random noise there may be other disturbing signal elements, or that the informative signal is comprised of several different components which we may want to observe, separate or remove. In these cases adding informative components in the model may result result in better performance than in the NML denoising approach.
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !