On the second stage, for the result image obtained from the first stage, multidimensional principal component analysis is performed to suppress the remaining noise, which avoids the vectorization to preserve the neighborhood information for MRI image and is helpful to improve the computation cost.
#Super denoising for windows full#
On the first stage, the 3D variant of the nonlocal means technique is employed to reduce the noise, which takes full advantage of the neighbor information between different 3D MRI slices and has the capability of exploiting the underlying structure in the multidimensional image. Therefore, this paper proposed a multidimensional structure preserving MRI denoising algorithm. From the aspect of super-resolution reconstruction, the noise image can be considered as the degraded version of the original image. Furthermore, the vectorization will make the structural information of image lost.Īctually, MRI is naturally a 3D image, which can be considered as tensor data on multidimensional space.
#Super denoising for windows windows#
Nevertheless, PCA on overlapping windows will reduce the computational efficiency. After denoising with optimized multicomponent nonlocal mean (OMNLM), the local PCA is conducted over small local windows instead of the whole image to overcome the drawback. The paper has developed a two-stage approach to improve the quality of MRI data. The drawback has limited the application of PCA in the field of image denoising. However, PCA requires that the number of images be bigger than the number of significant components of the image.
Therefore, has proposed an optimized blockwise NLM filter for 3D MRI.ĭue to its ability to perform decorrelation, PCA has also been used in image denoising. Nevertheless, the high computational burden has restricted its application for 3D MRI data. Since MRI image has multichannel nature, NLM has been modified to denoise MRI data where the similarity measure can be considered to combine the relative information between different slices. The restored pixel is considered as the weighted average of the intensities of all pixels within the neighborhood area. NLM exploits the redundancy of the neighborhood pixel to remove the noise. In particular, nonlocal means (NLM) filter has been used to denoise MRI image, achieving notable results. Statistical methods estimate the noise with maximum likelihood, linear minimum mean square error (LMMSE), Markov random process, and empirical Bayes. Transform methods employ some kinds of transformation to denoising MRI including wavelet transform and curvelet transform. Filtering methods remove noise with linear or nonlinear filters. Generally speaking, MRI denoising techniques can be classified as either filtering, transform, or statistical approach. Consequently, the denoising techniques are required to improve the quality of MRI. Such noise seriously degrades the acquisition of any quantitative measurements from the data. One of them is the random fluctuation of the MRI signal which is mainly due to thermal noise. However, MRI is affected by several artifacts and noise sources. IntroductionĪs a significant imaging technique, magnetic resonance imaging (MRI) provides very important information to research the tissues and organs in the human body with noninvasive style. The experiments have demonstrated that the proposed method has achieved better visual results and evaluation criteria than 3D-ADF, NLM3D, and OMNLM_LAPCA. The proposed algorithm takes full use of the block representation advantageous of NLM3D to restore the noisy slice from different neighboring slices and employs MPCA as a postprocessing step to remove noise further while preserving the structural information of 3D MRI. The two-stage MRI denoising algorithm proposed in this paper is based on 3D optimized blockwise version of NLM and multidimensional PCA (MPCA). Recently nonlocal means (NLM) and its variants have been applied in the various scientific fields extensively due to its simplicity and desirable property to conserve the neighborhood information.