Undecimated double density dual tree wavelet transform based image denoising using a subband adaptive threshold

Abstract

This paper presents a novel method for image denoising based on the undecimated double density dual tree discrete wavelet transform (UDDDT-DWT). The critically sampled discrete wavelet transform (DWT) suffers from the drawbacks of being shift-variant and lacking the capacity to process directional information in images. The double density dual tree discrete wavelet transform (DDDT-DWT) is an approximately shift-invariant transform capturing directional information. The UDDDT-DWT is an improvement of the DDDT-DWT, making it exactly shift-invariant. An adaptive threshold is found by analyzing the statistical parameters of each subband and it is applied using modified soft thresholding. Experimental results over a range of noise variances indicate that proposed method performs better than other state of the art methods considered. This paper presents a novel method for image denoising based on the undecimated double density dual tree discrete wavelet transform (UDDDT-DWT). The critically sampled discrete wavelet transform (DWT) suffers from the drawbacks of being shift-variant and lacking the capacity to process directional information in images. The double density dual tree discrete wavelet transform (DDDT-DWT) is an approximately shift-invariant transform capturing directional information. The UDDDT-DWT is an improvement of the DDDT-DWT, making it exactly shift-invariant. An adaptive threshold is found by analyzing the statistical parameters of each subband and it is applied using modified soft thresholding. Experimental results over a range of noise variances indicate that proposed method performs better than other state of the art methods considered.