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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. High-band 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 derive optimal MMSE 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.
Li et al. (Mon,) studied this question.