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首页> 外文会议>2017 International Conference on Inventive Computing and Informatics >Performance study of despeckling algorithm for different wavelet transforms
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Performance study of despeckling algorithm for different wavelet transforms

机译：不同小波变换去斑点算法的性能研究

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摘要

This paper proposes an efficient approach for removing speckle noise from images by using threshold estimation methods, based on Double Density Wavelet Transform (DDWT). The performance of the proposed de-speckling algorithm is well enhanced by applying two different threshold estimation techniques at different decomposition levels of DDWT. The choice of estimation of the threshold value is carried out by VisuShrink and Normal Shrink rules. In this technique, the image is decomposed into nine sub bands using DDWT. The threshold values are estimated from each of the detail sub band in such a way that, using VisuShrink for first level DDWT decomposition and Normal Shrink for higher levels. After that, soft thresholding technique is used for noise suppression. Subsequently the inverse transform is applied to obtain the denoised image. Also the performance of this approach is investigated on Dual tree real, Dual tree complex, Double Density Dual tree real and Double Density dual tree complex wavelet transforms till fifth level decomposition. Computer simulations are carried out to investigate the performance of the proposed method and the results are compared with the existing methods. The results show that the proposed method achieves excellent performance.
机译：本文提出了一种基于阈值估计的基于双密度小波变换（DDWT）的图像去斑噪声消除方法。通过在DDWT的不同分解级别上应用两种不同的阈值估计技术，可以很好地提高所提出的去斑点算法的性能。阈值估计的选择由VisuShrink和Normal Shrink规则执行。在该技术中，使用DDWT将图像分解为9个子带。从每个细节子带估计阈值，以这种方式，将VisuShrink用于第一级DDWT分解，将Normal Shrink用于正常级。之后，将软阈值技术用于噪声抑制。随后，应用逆变换以获得经去噪的图像。还研究了该方法在双树实数，双树复数，双密度双树实数和双密度双树复数小波变换到第五级分解之前的性能。通过计算机仿真研究了该方法的性能，并将结果与​​现有方法进行了比较。结果表明，该方法具有良好的性能。

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