Multilevel Threshold Based Image Denoising in Curvelet Domain

In this paper, we propose a multilevel thresholding technique for noise removal in curvelet transform domain which uses cycle-spinning. Most of uncorrelated noise gets removed by thresholding curvelet coefficients at lowest level, while correlated noise gets removed by only a fraction at lower levels, so we used multilevel thresholding on curvelet coefficients. The threshold in the proposed method depends on the variance of curvelet coefficients, the mean and the median of absolute curvelet coefficients at a particular level which makes it adaptive in nature. Results obtained for 2-D images demonstrate an improved performance over other recent related methods available in literature.

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