Abstract
This paper presents an algorithm based on nonsubsampled contourlet transform (NSCT) and Stein's unbiased risk estimate with a linear expansion of thresholds (SURE-LET) approach for intensity image denoising. First, we analyzed the multiplicative noise model of intensity image and make the non-logarithmic transform on the noisy signal. Then, as a multiscale geometric representation tool with multi-directivity and shift-invariance, NSCT was performed to capture the geometric information of images. Finally, SURE-LET strategy was modified to minimize the estimation of the mean square error between the clean image and the denoised one in the NSCT domain. Experiments on real intensity images show that the algorithm has excellent denoising performance in terms of the peak signal-to-noise ratio (PSNR), the computation time and the visual quality.
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