Spatially Adaptive Image Denoising Under OverComplete Expansion

This paper presents a novel wavelet-based image denoising algorithm under overcomplete expansion. In order to optimize the denoising performance, we make a systematic study of both signal and noise characteristics under overcomplete expansion. Highband coefficients are viewed as the mixture of non-edge class and edge class observing different probability models. Based on improved statistical modeling of wavelet coefficients, we deriveoptimalMMSE estimation strategies to suppress noise for both non-edge and edge coefficients. We have achieved fairly better objective performance than most recently-published wavelet denoising schemes.

• Type: Conference
• Year: 2000
• Where: ICIP
• Authors: Xin Li, Michael T. Orchard
• Keywords: ICIP 2000, Image Denoising Algorithm, Image Processing, Wavelet Coefficients, Wavelet Denoising Schemes