Abstract
In this paper, an extension of the interscale SURE-LET approach exploiting the interscale and intrascale dependencies of wavelet coefficients is proposed to improve denoising performance. This method incorporates information on neighbouring coefficients into the linear expansion of thresholds (LETs) without additional parameters to capture the texture characteristics of this image. The resulting interscale-intrascale wavelet estimator consists of a linear expansion of multivariate thresholding functions, whose parameters are optimized thanks to a multivariate Stein's unbiased risk estimate (SURE). Some experimental results are given to demonstrate the strength of the proposed method.
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