title: Spatially adaptive wavelet denoising using the minimum description length principle.
abstract: This paper presents a new spatially adaptive wavelet denoising method. Based on a doubly stochastic process model of wavelet coefficients, the method gives a new threshold, which varies spatially according to the variances of the coefficients, using the minimum description length (MDL) principle. The new threshold is not only easier to analyze since it is in a closed form, but also provides more facility for future compression than several other methods, almost without deteriorating mean square error risk.
authors.affiliation: Tsinghua University, Beijing 100084, China. xiejiecheng97@mails.tsinghua.edu.cn
authors.forename: Jiecheng
authors.initials: J
authors.lastname: Xie
authors.forename: Dali
authors.initials: D
authors.lastname: Zhang
authors.forename: Wenli
authors.initials: W
authors.lastname: Xu
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D015233:Models, Statistical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes