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Medical Image Denoising Using Adaptive Threshold Based on Contourlet Transform
S.Satheesh,KVSVR Prasad
Advanced Computing : an International Journal , 2011,
Abstract: Image denoising has become an essential exercise in medical imaging especially the Magnetic ResonanceImaging (MRI). This paper proposes a medical image denoising algorithm using contourlet transform.Numerical results show that the proposed algorithm can obtained higher peak signal to noise ratio(PSNR) than wavelet based denoising algorithms using MR Images in the presence of AWGN.
Medical Image Denoising using Adaptive Threshold Based on Contourlet Transform  [PDF]
S. Satheesh,KVSVR Prasad
Computer Science , 2011, DOI: 10.5121/acij.2011.2205
Abstract: Image denoising has become an essential exercise in medical imaging especially the Magnetic Resonance Imaging (MRI). This paper proposes a medical image denoising algorithm using contourlet transform. Numerical results show that the proposed algorithm can obtained higher peak signal to noise ratio (PSNR) than wavelet based denoising algorithms using MR Images in the presence of AWGN.
Performance Analysis of Modified Nonsubsampled Contourlet Transform for Image Denoising  [cached]
H. Devanna,G.A.E. Satish Kumar
Research Journal of Applied Sciences, Engineering and Technology , 2011,
Abstract: In this study, we develop modified Nonsubsampled Contourlet Transform (NSCT). The construction of NSCT is based on new nonsubsampled pyramid structure and Nonsubsampled Directional Filters (NSDF). The result is improved in flexible multiage, multidirectional and shift invariant image decomposition that can be effectively implemented through Matlab. The modified NSCT, it proposed to distinguish noise and edge effectively. So we assess the performance of the modified NSCT in image denoising applications. In this application the NSCT compares favorably to other existing method.
The Image Denoising with Correlation Based on Redundant Contourlet Transform
基于冗余Contourlet变换的图像相关法去噪

CHENG Guang-quan,CHENG Li-zhi,
程光权
,成礼智

中国图象图形学报 , 2008,
Abstract: Contourlet transform(CT) is a method of multiscale geometric analysis,which can result in a flexible multi-resolution,local,and directional image expansion.But the Contourlet transform is not shift-invariant,that will cause pseudo-Gibbs phenomena around singularities in image denoising.In this paper we apply redundant contourlet transform with shift-invariant to image denosing,which can capture the intrinsic geometrical structure of image.Meanwhile,we consider the dependencies between the coefficients and their parents in detail.We propose a method of image denoising based on redundant contourlet with bivariate shrinkage rules.The experimental results show that our method can obtain higher PSNR value and better visual effect compared with other methods.
Adaptive thresholding for image denoising via sationary Contourlet transform
基于平稳Contourlet变换的自适应阈值去噪*

CHENG Guang-quan,CHENG Li-zhi,
程光权
,成礼智

计算机应用研究 , 2008,
Abstract: Stationary Contourlet transform with shifl-invariant was applied to image denoising, which could capture the intrinsic geometrical structure of image. Meanwhile, the adaptive Bayes threshold with hard threshold function was implemented to image denoising. The experimental results show that the method can get higher PSNR value and better visual effect compared with other methods.
Image Denoising Using Nonsampled Contourlet Transform and Muiti-scale Thresholds
一种新的非下采样Contourlet域图像去噪算法

FU Zhong-kai,WANG Xiang-yang,ZHENG Hong-liang,
付仲凯
,王向阳,郑宏亮

计算机科学 , 2009,
Abstract: The nonsubsampled contourlet transform is a fully shift invariant, multi-scale, and multi-direction expansion that has better directional frequency localization and a fast implementation. We proposed a novel image denoising method by incorporating the nonsubsampled contourlet transform. The fully shift invariant property and the high directional sensitivity of the nonsubsampled contourlet transform make the new method a very good choice for image denoising.Firstly, the image was decomposed in different subbands of frequency and orientation responses using the nonsubsampled contourlet transform. Then the multi-scale thresholds were computed according to noise distribution, and used to shrink the nonsubsampled contourlet coefficients. Finally, the modified nonsubsampled contourlet coefficients were transformed back into the original domain to get the denoised image. Simulation results show that the method can obtain higher peak-signal-to-noise ratio, compared with other recent image denoising methods, such as wavelet denoising and contourlet denoising.
The Translation Invariant Contourlet-like Transform for Image Denoising
基于平移不变性类Contourlet变换的图像去噪(英文)

LIAN Qiu-Sheng CHEN Shu-Zhen,
练秋生
,陈书贞

自动化学报 , 2009,
Abstract: The contourlet transform with anisotropy and directionality is a new extension to the wavelet transform.Because of its filter bank structure,the contourlet transform is not translation-invariant.In this paper,we propose the translation-invariant contourlet-like transform(TICLT)with lower redundancy than both the noILsubsampled co.ourlet transform(NSCT)and the translation invariant contourlet transform (TICT).The TICLT is constructed by combining the translation invariant Laplacian pyramid and undecimated directional filter banks.The undecimated directionaI filter banks.which satisfy the perfect reconstruction condition.are designed by the mapping approach using one-dimensional fractional splines orthogonal filter banks as prototype filters. We evaluate the performance of the TICLT in image denoising.Some comparisons with the state-of-the-art denoising methods are giyen to illustrate the potential of the TICLT.
The Application of a New All Phase Contourlet Discrete Transform on Image Denoising
一种新的全相位Contourlet离散变换在图像去噪中的应用

HOU Zheng-xin,BEN Liang,GUO Xu-jing,
侯正信
,任亮,郭旭静

中国图象图形学报 , 2008,
Abstract: This paper proposes a bran-new discrete transform called AP-Contourlet (All Phase Contourlet), the multi-scale decomposition and reconstruction of which are based on the APDCT (All Phase DCT) sub-band filtering and the APIDCT (All Phase IDCT) interpolation for optimizing denoising effection of Contourlet. In addition, a kind of all phase directional filter bank (APDFB) based on DCT is used for directional filtering of the AP-Contourlet. The APDFB is possessed of excellent directional selectivity, and reconstruction algorithm is so simple that only adding the directional images is needed. Experiments in image de-noising have shown that the performance of the proposed AP-Contourlet is obviously superior to the conventional Contourlet both in vision and in signal to noise ratio (SNR).
Image Denoising Based on Nonsubsampled Contourlet Transform and Bivariate Model
基于非下采样Contourlet变换和双变量模型的图像去噪

Bian Ce Zhong Hua Jiao Li-cheng,
边策
,钟桦,焦李成

电子与信息学报 , 2009,
Abstract: This paper proposes a new image denoising method based on the NonsubSampled Contourlet Transform(NSCT) and the bivariate model under the framework of Bayesian MAP estimation theory. The proposed algorithm uses the NSCT's advantages of translation-invariant and multidirection-selectivity,exploits the intra-scale and inter-scale correlations of NSCT coefficients,and elaborates the method of noise estimation. Compared with some current outstanding denoising methods,the simulation results and analysis show that...
Image denoising based on Contourlet transform and total variation
一种基于Contourlets变换和全变差的图像去噪方法

SHEN Wei-yan,WEI Zhi-hui,DUAN Qiu-feng,
沈维燕
,韦志辉,段秋枫

计算机应用 , 2008,
Abstract: In order to overcome the oscillation phenomenon and preserve more details in image denoising, a recently proposed Contourlet transform was used, which was an efficient directional multi-resolution image representation and behaved much better than the wavelet transform in capturing fine details, e.g., the edge and the texture. Unlike most conventional Total Variation (TV) minimization techniques in image processing, the TV regularity in this paper was directly imposed on the Contourlet domain. The proposed method performs efficient image denoising with fewer artifacts, and achieves a better compromise between noise removal and edge preservation.
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