Real Oriented Dual Tree Wavelet Transform with an Optimal Threshold using Neighbor Coefficients and Gaussian Bilateral Filter for Image Denoising
Mohan Laavanya*, Marappan Karthikeyan**
*Information and Communication Engineering, Anna University, Chennai, India. firstname.lastname@example.org
**Tamilnadu College of Engineering, Karumathampatti, Coimbatore, India
Online published on 23 March, 2017.
A novel method for image denoising using real oriented 2-D Dual Tree Wavelet Transform (DTWT) is proposed. In this approach image is contaminated by white Gaussian noise. The optimal threshold which is data driven is determined using the Neigh Sure that uses Stein's Unbiased Risk Estimator (SURE) with minimum risk for the whole noisy image. Real oriented 2-D DTWT of a noisy image results in six sub-bands that is non-separable. The transformed image is soft thresholded using the optimal threshold value. Further the denoising performance is improved by using the Gaussian bilateral filter. The experimental result shows that the proposed method is better in terms of Peak Signal to Noise Ratio (PSNR) than other existing techniques.
Bilateral filter, Gaussian filter, image denoising, neighboring coefficients, real oriented dual tree wavelet transform, stein's unbiased risk estimator.