An Innovative Image Denoising Method Using Curvelet Transform and Histogram Segmentation
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Abstract
A new image denoising method based on Curvelet transform and histogram segmentation is proposed. This paper first explores the concept and the properties of the Curvelet transform for curved singularities analysis then applies Curvelet transform and histogram segmentation to estimate optimum threshold for image denoising. In the simulations, the Wrap (Wrapping-based transform) algorithm was used to realize the Curvelet transform, which adds a wrap step to the Unequally Spaced Fast Fourier Transform (USFFT) method. The simulation results show the denoising effectiveness of the proposed method, show that Curvelet transform has a better denoising result and a certain increase in PSNR (Peak Signal-to-Noise Ratio), especially for the images those contain curved singularities.
Keywords
Curvelet transform, Image denoise, Histogram segmentation
Article Details
References
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[2] Miller. M and Kingsbury, Nick; Image Denoising Using Derotated Complex Wavelet Coefficients, IEEE Trans. Image Processing vol. 17, No. 9, pp. 1500-1511, 2008.
[3] Kour G, Singh SP; Image Decomposition Using Wavelet Transform. International Journal Of Engineering And Computer Science vol. 2, pp. 3477-3480, 2013.
[4] Jean-Luc Starck, Emmanuel J. Candès, and David L. Donoho; The Curvelet Transform for Image Denoising, IEEE Transactions on image processing, vol. 11, no. 6, june 2002.
[5] Min Li , Xiaoli Sun; Curvelet Shrinkage Based Iterative Regularization Method for Image Denoising, 12th International Conference on Computational Intelligence and Security (CIS), pp. 103 – 106, 2016.
[6] K. S. Jeen Marseline, C. Meena; Combined Curvelet and ASF with neural network for denoising sonar images, 2015 International Conference on Advanced Computing and Communication Systems, pp. 1-6, 2015.
[7] Guillaume T, et al; Application of the Curvelet Transform for Clutter and Noise Removal in GPR Data; IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 10 , Issue 10, pp. 4280 – 4294, 2017.
[8] Paul H, Alin A; Mohammed E. Al-Mualla; David B, Contrast Sensitivity of the Wavelet, Dual Tree Complex Wavelet, Curvelet, and Steerable Pyramid Transforms; IEEE Transactions on Image Processing, Vol.2, Issue 6, pp. 2739 – 2751, 2016.