Block thresholding image denoising with dual-tree complex wavelet transform

Abstract

As a new tool of signal and image analysis, the dual-tree complex wavelet transform (DTCWT) has been found very useful for a lot of applications in machine learning and pattern recognition. In this paper, we present a method for denoising of images corrupted with additive white Gaussian noise, which employs DTCWT on noisy image to obtain complex coefficients with properties of approximate shift invariance and directional selection. Furthermore, our method adopts block thresholding scheme in denoising procedure, which can empirically choose the optimal block size and threshold at each resolution level by minimizing Stein's unbiased risk estimate. Experimental results on several benchmark images show that our method outperforms most conventional term by term or block thresholding based methods for eliminating different levels of noise. Moreover, its denoising result is compared to state-of-the-art denoising methods with finer structures preservation.