Signal-dependent noise removal in the undecimated wavelet domain

2002 ◽  
Author(s):  
Argenti ◽  
Torricelli ◽  
Alparone
Keyword(s):  
Noise Removal ◽  
Wavelet Domain ◽  
Undecimated Wavelet

scholarly journals Local Complexity Estimation Based Filtering Method in Wavelet Domain for Magnetic Resonance Imaging Denoising

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 401 ◽  
Author(s):  
Izlian Y. Orea-Flores ◽  
Francisco J. Gallegos-Funes ◽  
Alfonso Arellano-Reynoso
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Additive White Gaussian Noise ◽  
Noise Removal ◽  
Wavelet Domain ◽  
Resonance Imaging ◽  
Comparative Performance ◽  
Filtering Method ◽  
Local Complexity ◽  
Complexity Estimation

In this paper, we propose the local complexity estimation based filtering method in wavelet domain for MRI (magnetic resonance imaging) denoising. A threshold selection methodology is proposed in which the edge and detail preservation properties for each pixel are determined by the local complexity of the input image. In the proposed filtering method, the current wavelet kernel is compared with a threshold to identify the signal- or noise-dominant pixels in a scale providing a good visual quality avoiding blurred and over smoothened processed images. We present a comparative performance analysis with different wavelets to find the optimal wavelet for MRI denoising. Numerical experiments and visual results in simulated MR images degraded with Rician noise demonstrate that the proposed algorithm consistently outperforms other denoising methods by balancing the tradeoff between noise suppression and fine detail preservation. The proposed algorithm can enhance the contrast between regions allowing the delineation of the regions of interest between different textures or tissues in the processed images. The proposed approach produces a satisfactory result in the case of real MRI denoising by balancing the detail preservation and noise removal, by enhancing the contrast between the regions of the image. Additionally, the proposed algorithm is compared with other approaches in the case of Additive White Gaussian Noise (AWGN) using standard images to demonstrate that the proposed approach does not need to be adapted specifically to Rician or AWGN noise; it is an advantage of the proposed approach in comparison with other methods. Finally, the proposed scheme is simple, efficient and feasible for MRI denoising.

scholarly journals Noise Removal for Medical X-ray Images in Wavelet Domain

2006 ◽  
Vol 126 (2) ◽  
pp. 237-244 ◽  
Author(s):  
Ling Wang ◽  
Jianming Lu ◽  
Yeqiu Li ◽  
Takashi Yahagi ◽  
Takahide Okamoto
Keyword(s):  
Noise Removal ◽  
Wavelet Domain ◽  
X Ray

An evaluation of Bayesian estimators and PDF models for despeckling in the undecimated wavelet domain

2010 ◽  
Author(s):  
Luciano Alparone ◽  
Fabrizio Argenti ◽  
Tiziano Bianchi ◽  
Alessandro Lapini
Keyword(s):  
Wavelet Domain ◽  
Undecimated Wavelet ◽  
Bayesian Estimators

scholarly journals Denoising auto-encoding priors in undecimated wavelet domain for MR image reconstruction

2021 ◽  
Vol 437 ◽  
pp. 325-338
Author(s):  
Siyuan Wang ◽  
Junjie Lv ◽  
Zhuonan He ◽  
Dong Liang ◽  
Yang Chen ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Wavelet Domain ◽  
Mr Image ◽  
Undecimated Wavelet ◽  
Mr Image Reconstruction

scholarly journals Noise removal for medical X-ray images in wavelet domain

2008 ◽  
Vol 163 (3) ◽  
pp. 37-46 ◽  
Author(s):  
Ling Wang ◽  
Jianming Lu ◽  
Yeqiu Li ◽  
Takashi Yahagi ◽  
Takahide Okamoto
Keyword(s):  
Noise Removal ◽  
Wavelet Domain ◽  
X Ray
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