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![Noisy ultrasound image and denoised images by different methods. (a) Noisy image (PSNR = 24.019 dB). (b) Denoised image by CWT method [3] (PSNR = 27.019 dB). (c) Denoised image by curvelet transform [12] (PSNR = 30.053 dB). (d) Denoised image by the proposed method (PSNR = 31.331 dB).](profile/Ashish-Khare-6/publication/220584999/figure/fig3/AS:340785842147328@1458261085029/Noisy-ultrasound-image-and-denoised-images-by-different-methods-a-Noisy-image-PSNR.png)
Noisy ultrasound image and denoised images by different methods. (a) Noisy image (PSNR = 24.019 dB). (b) Denoised image by CWT method [3] (PSNR = 27.019 dB). (c) Denoised image by curvelet transform [12] (PSNR = 30.053 dB). (d) Denoised image by the proposed method (PSNR = 31.331 dB).
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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...
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Citations
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Most of medical images not only have noise but also have blur. This problem reduces the quality of images and influences diagnostic process of medical specialists because a small detail in a medical image is very useful for treatment process. This chapter explores the new generation wavelets, which provides the basic framework for the development of adaptive techniques to improve the quality of medical images. The process of the method for improving medical images includes: decompose of medical images in nonsubsampled contourlet domain and calculate the coefficients of Bayesian thresholding combined with Lucy Richard to reconstruct the medical images. For demonstrating the superiority of the method, the results of the proposed method are compared with the results of the other methods in new generation wavelet domain.
