This paper describes wavelet thresholding for image denoising under the framework provided by Statistical Learning Theory aka Vapnik-Chervonenkis (VC) theory. Under the framework of VC-theory, wavelet thresholding amounts to ordering of wavelet coefficients according to their relevance to accurate function estimation, followed by discarding insignificant coefficients. Existing wavelet thresholding methods specify an ordering based on the coefficient magnitude, and use threshold(s) derived under gaussian noise assumption and asymptotic settings. In contrast, the proposed approach uses ordering better reflecting statistical properties of natural images, and VC-based thresholding developed for finite sample settings under very general noise assumptions. A tree structure is proposed to order the wavelet coefficients based on its magnitude, scale and spatial location. The choice of a threshold is based on the general VC method for model complexity control. Empirical results show that the proposed method outperforms Donoho's level dependent thresholding techniques and the advantages become more significant under finite sample and non-gaussian noise settings.
|Original language||English (US)|
|Title of host publication||IEEE International Conference on Image Processing|
|State||Published - Dec 1 2000|
|Event||International Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada|
Duration: Sep 10 2000 → Sep 13 2000
|Other||International Conference on Image Processing (ICIP 2000)|
|Period||9/10/00 → 9/13/00|