Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear total variation based noise removal algorithms
Proceedings of the eleventh annual international conference of the Center for Nonlinear Studies on Experimental mathematics : computational issues in nonlinear science: computational issues in nonlinear science
An Algorithm for Total Variation Minimization and Applications
Journal of Mathematical Imaging and Vision
New Image Denoising Method Based Wavelet and Curvelet Transform
ICIE '09 Proceedings of the 2009 WASE International Conference on Information Engineering - Volume 01
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
The discrete wavelet transform: wedding the a trous and Mallatalgorithms
IEEE Transactions on Signal Processing
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Wavelet-based Rician noise removal for magnetic resonance imaging
IEEE Transactions on Image Processing
The curvelet transform for image denoising
IEEE Transactions on Image Processing
Gray and color image contrast enhancement by the curvelet transform
IEEE Transactions on Image Processing
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
An information fidelity criterion for image quality assessment using natural scene statistics
IEEE Transactions on Image Processing
Computers and Electrical Engineering
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In medical images noise and artifacts are introduced due to the acquisition techniques and systems. Due to the noise present in the medical images, experts may not be able to draw correct and useful information from the images. The paper proposes a noise reduction method for both computed tomography (CT) and magnetic resonance imaging (MRI) which fuses the images (i) denoised by total variation (TV) method, (ii) denoised by curvelet based method and (iii) the edge information, where edge information is extracted from the noise residual of TV method by processing it through curvelet transform. The performance of the proposed method is evaluated on real brain CT and MRI images and results show significant improvement not only in noise suppression but also in edge preservation.