Abstract
To circumvent the visual distortions due to the discontinuity of the curvelet hard-thresholding and the constant reconstruction deviation resulted from the soft-thresholding, we considered the distribution characteristics of noise coefficients in each curvelet subband and the desired properties of an ideal curvelet thresholding scheme, and developed a new thresholding function using Chi-square cumulative distribution function. Further, in order to eliminate surrounding effect inherent in curvelet thresholding denoising framework and simultaneously achieve a better balance between detail preservation and noise removal, a novel curvelet denoising method based on data fusion theory is proposed. It is realized by operating the partial differential equation denoising method on the small scales and our thresholding method on the big ones respectively. Theoretical analysis and simulation results show that the proposed denoising method outperforms the soft and hard thresholding denoising methods in terms of the denoising effect and visual quality.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call