Rician Noise Removal Research Articles

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

Retraction Note: Adaptive hexagonal fuzzy hybrid filter for Rician noise removal in MRI images

Rician Noise Removal in Low-noise Condition via a Deep Unfolding Network

Nuclear magnetic resonance (NMR) signals as the key role of research on denoising, are mainly influenced by Rician noise. Restoring clean Rician medical images from signal-related Rician noise is a challenging task with great practical significance. In this paper, an energy function based on MAP estimation is proposed, mainly for conditions of low noise, where the noise distribution is approximately Gaussian. Then the mathematical model is embedded into the network structure to realize the knot of knowledge-driven and network learning. The experiment shows that the proposed model only uses simple and lightweight network modules and a small amount of training data, which has a better denoising effect and has achieved satisfactory results under the two evaluation indexes.

A spatially variant high-order variational model for Rician noise removal.

Rician noise removal is an important problem in magnetic resonance (MR) imaging. Among the existing approaches, the variational method is an essential mathematical technique for Rician noise reduction. The previous variational methods mainly employ the total variation (TV) regularizer, which is a first-order term. Although the TV regularizer is able to remove noise while preserving object edges, it suffers the staircase effect. Besides, the adaptability has received little research attention. To this end, we propose a spatially variant high-order variational model (SVHOVM) for Rician noise reduction. We introduce a spatially variant TV regularizer, which can adjust the smoothing strength for each pixel depending on its characteristics. Furthermore, SVHOVM utilizes the bounded Hessian (BH) regularizer to diminish the staircase effect generated by the TV term. We develop a split Bregman algorithm to solve the proposed minimization problem. Extensive experiments are performed to demonstrate the superiority of SVHOVM over some existing variational models for Rician noise removal.

Variational Rician Noise Removal via Splitting on Spheres

Variational Rician Noise Removal via Splitting on Spheres

Flexible parameter selection methods for Rician noise removal with convergence guarantee

Restoring images corrupted by Rician noise is a challenging issue in the field of medical image processing. In the existing variational methods, there is a balancing parameter between the regularization term and the fidelity term. However, it is very hard to find the optimal parameter. In this paper, we study the total variation-based Rician noise removal model with spatially varying parameters. We propose flexible and automatic parameter selection strategies to balance the regularization extent between different kinds of image regions. A modified alternating direction method of multipliers is derived to solve the non-convex model efficiently. Theoretically, we prove that if a selection strategy satisfies some reasonable conditions, the convergence of the proposed algorithm is guaranteed. Numerical results demonstrate that the proposed method with automatic parameter selection can better preserve the structures and fine textures than other closely related methods.

A Rician Noise Prediction and Removal Model for MRI Head Scans using wavelet based Non-Local Median Filter

In the medical imaging field, anatomical structure preservation is a difficult task during the denoising process. Magnetic resonance imaging (MRI) scanner corrupts the images by the Rician noise during the acquisition process. Rician noise affects the diagnosis and treatment planning for the subjects. Regenerating noise-free images is a time-consuming process with limited MR scanner resources available in developing countries. Therefore, the medical industry utilizes the advancement of computer-aided automatic denoising methods. This article presents a novel denoising method for Rician noise using a wavelet-based non-local median filtering (WNLMed) technique. The work contains three phases: noise estimation, wavelet thresholding using a lifting scheme, and non-local median filter (NLMed). Materials used for this experiment are collected from the Brainweb repositories and tested with validation metrics such as normalized absolute error (NAE), peak signal to noise ratio (PSNR), structure similarity index measure (SSIM), the figure of merit (FOM), and compared with the state-of-the-art methods. The method yields high PSNR value than other methods.

Patch-Based Weighted SCAD Prior for Rician Noise Removal

Patch-Based Weighted SCAD Prior for Rician Noise Removal

A nonlocal enhanced Low-Rank tensor approximation framework for 3D Magnetic Resonance image denoising

Magnetic Resonance Imaging (MRI) has become an increasingly essential tool in clinical detection and diagnosis for its ability to disclose the distinctive information of human anatomic structures in vivo. However, the image intensity in magnitude MRI images is frequently corrupted by Rician noise which is inherent to the acquisition process. This study aimed to propose a denoising model for 3D MR image restoration based on low-rank tensor approximation with a Logarithmic-Sum regularization framework. Nonlocal similarity of images was exploited with grouping and block matching techniques, and tensor decomposition approaches were utilized taking consideration of multi-dimensional structural dependence conservation. The low-rank tensor approximation problem was regulated by introducing a Logarithmic-Sum trace norm, which thresholded the singular value spectrum adaptively with different thresholds. Noise removal was processed through the low-rank approximation, and the denoised tensor blocks were aggregated to restore the clear images. Numerical experiments were conducted under comprehensive noise conditions for 3D MR volumetric datasets. Through denoising comparison, the results demonstrated that the proposed algorithm achieves state-of-the-art performance for Rician noise removal with excellent detail preservation..

Recasted nonlinear complex diffusion method for removal of Rician noise from breast MRI images

The evolution of magnetic resonance imaging (MRI) leads to the study of the internal anatomy of the breast. It maps the physical features along with functional characteristics of selected regions. However, its mapping accuracy is affected by the presence of Rician noise. This noise limits the qualitative and quantitative measures of breast image. This paper proposes recasted nonlinear complex diffusion filter for sharpening the details and removal of Rician noise. It follows maximum likelihood estimation along with optimal parameter selection of complex diffusion where the overall functionality is balanced by regularization parameters. To make recasted nonlinear complex diffusion, the edge threshold constraint “k” of diffusion coefficient is reformed. It is replaced by the standard deviation of the image. It offers a wide range of threshold as variability present in the image with respect to edge. It also provides an automatic selection of “k” instead of user-based value. A series of evaluation has been conducted with respect to different noise ratios further quality improvement of MRI. The qualitative and quantitative assessments of evaluations are tested for the Reference Image Database to Evaluate Therapy Response (RIDER) Breast database. The proposed method is also compared with other existing methods. The quantitative assessment includes the parameters of the full-reference image, human visual system, and no-reference image. It is observed that the proposed method is capable of preserving edges, sharpening the details, and removal of Rician noise.

Residual encoder-decoder up-sampling for structural preservation in noise removal

While denoising 3D MRI, structural preservation is a very critical process in the medical region. However, Rician noise in MRI has affected the image quality which is complicated for diagnosing the disease like a brain tumor. So the Rician noise removal process is introduced in this article using filters based method named Residual Encoder-Decoder Up-sampling Non-Similar Wassertein Generative Adversarial Network (REDUPNSWGAN) which preserves the structural similarity between the neighbor-hood slices in 3D configuration is utilized as GPU. Residual auto-encoders are connected with De-convolution processes that are sent to the Generator Net (GNET). Furthermore, to reduce the level of Huber loss, the perceptual loss for similarity is implemented by the extraction of the feature space using Average-pool Net (AVGNET) which is incorporated with Huber loss, signed structural non-similar loss and Wassertein Adversarial loss to form the REDUPNSWGAN loss. Experimentally, this proposed method shows better performance quality based on mean PSNR than REDWGANVGG19 and also time complexity based on training and testing period which shows better than REDWGANVGG19. Particularly, the proposed network Residual Encoder-Decoder Upsampling Non-Similar REDUPNSWGAN reduces the noise level and preserves the structural similarity.

Joint low-rank prior and difference of Gaussian filter for magnetic resonance image denoising.

The low-rank matrix approximation (LRMA) is an efficient image denoising method to reduce additive Gaussian noise. However, the existing low-rank matrix approximation does not perform well in terms of Rician noise removal for magnetic resonance imaging (MRI). To this end, we propose a novel MR image denoising approach based on the extended difference of Gaussian (DoG) filter and nonlocal low-rank regularization. In the proposed method, a novel nonlocal self-similarity evaluation with the tight frame is exploited to improve the patch matching. To remove the Rician noise and preserve the edge details, the extended DoG filter is exploited to the nonlocal low-rank regularization model. The experimental results demonstrate that the proposed method can preserve more edge and fine structures while removing noise in MR image as compared with some of the existing methods.

Rician noise removal via weighted nuclear norm penalization

Rician noise is a common noise that naturally appears in Magnetic Resonance Imaging (MRI) images. Low rank matrix approximation approaches have been widely used in image processing, which takes advantage of the non-local self-similarity between patches in a natural image. The weighted nuclear norm minimization method as a low rank matrix approximation approach has shown to be an effective approach for image denoising. Inspired by this, we propose in this paper a maximum a posteriori (MAP) model with the weighted nuclear norm as a regularization constraint to remove Rician noise. The MAP data fidelity term has a Lipschitz continuous gradient and the weighted nuclear norm can be efficiently minimized. We propose an iterative weighted nuclear norm minimization algorithm (IWNNM) to solve the proposed non-convex model and analyze the convergence of our algorithm. The computational results show that our proposed method is promising in restoring images corrupted with Rician noise.

Adaptive trainable non‐linear reaction diffusion for Rician noise removal

Rician noise reduction is an essential issue in magnetic resonance imaging (MRI). Recently, learning-based methods have achieved great success in dealing with image restoration problems, which provide fast inference and good performance. One limitation of these methods, however, is that the training procedure is usually noise-level dependent, i.e. the trained models are bound to a specific noise level and lack the ability to automatically adapt to different noise levels. In this study, the authors propose a variational model for Rician noise removal by integrating a noise adaption function into the field of experts image prior, which can adapt to different noise levels. Instead of directly solving the energy minimisation problem, the authors unroll the gradient descent step of the energy functional for several iterations, the time-dependent parameters of which can be learned through a supervised training process. The authors call this methodology as the noise adaptive trainable non-linear reaction–diffusion model. The proposed methodology is robustness against noise level changing and noise distributions. Experimental results over T 1 -, T 2 - and PD-weighted MRI data set demonstrate that the proposed model can achieve superior performance compared with other methods in terms of both the peak signal-to-noise ratio and the structural similarity index.

Implementation of Iterative bilateral filtering for removal of Rician noise in MR images using FPGA

Magnetic resonance image noise reduction is important to process further and visual analysis. Bilateral filter is denoises image and also preserves edge. It proposes Iterative bilateral filter which reduces Rician noise in the magnitude magnetic resonance images and retains the fine structures, edges and it also reduces the bias caused by Rician noise. The visual and diagnostic quality of the image is retained. The quantitative analysis is based on analysis of standard quality metrics parameters like peak signal-to-noise ratio and mean structural similarity index matrix reveals that these methods yields better results than the other proposed denoising methods for MRI. Problem associated with the method is that it is computationally complex hence time consuming. It is not recommended for real time applications. To use in real time application a parallel implantation of the same using FPGA is proposed.

A Modified Higher-Order Singular Value Decomposition Framework With Adaptive Multilinear Tensor Rank Approximation for Three-Dimensional Magnetic Resonance Rician Noise Removal.

The magnetic resonance (MR) images are acknowledged to be inevitably corrupted by Rician distributed noise, which adversely affected the image quality for diagnosis purpose. However, the traditional denoising methods may recover the images from corruptions with severe loss of detailed structure and edge information, which would affect the lesion detections and diagnostic decision making. In this study, we challenged improving the Rician noise removal from three-dimensional (3D) MR volumetric data through a modified higher-order singular value decomposition (MHOSVD) method. The proposed framework of MHOSVD involved a parameterized logarithmic nonconvex penalty function for low-rank tensor approximation (LRTA) algorithm optimization to suppress the image noise in MR dataset. Reference cubes were extracted from the noisy image volume, and block matching was performed according to nonlocal similarity for a fourth-order tensor construction. Then the LRTA problem was implemented by tensor factorization approaches, and the ranks of unfolding matrices along different modes of the tensor were estimated utilizing an adaptive nonconvex low-rank method. The denoised MR images were finally restored through aggregating all recovered cubes. We investigated the proposed algorithm MHOSVD on both the synthetic and real clinic 3D MR images for Rician noise removal, and relative results demonstrated that the MHOSVD can recover images with fine structures and detailed edge preservation with heavy noise even as high as 15% of the maximum intensity. The experimental results were also compared along with several classical denoising methods; the MHOSVD exhibited a sufficient improvement in noise-removal performance at various noise conditions in terms of different measurement indices such as peak signal-to-noise ratio and structural similarity index metrics. Based upon the comparison, the proposed MHOSVD has proved a relative state-of-the-art performance with excellent detailed structure reservation.

Sparse MR Image Reconstruction Considering Rician Noise Models: A CNN Approach.

Compressive sensing (CS) provides a potential platform for acquiring slow and sequential data, as in magnetic resonance (MR) imaging. However, CS requires high computational time for reconstructing MR images from sparse k-space data, which restricts its usage for high speed online reconstruction and wireless communications. Another major challenge is removal of Rician noise from magnitude MR images which changes the image characteristics, and thus affects the clinical usefulness. The work carried out so far predominantly models MRI noise as a Gaussian type. The use of advanced noise models primarily Rician type in CS paradigm is less explored. In this work, we develop a novel framework to reconstruct MR images with high speed and visual quality from noisy sparse k-space data. The proposed algorithm employs a convolutional neural network (CNN) to denoise MR images corrupted with Rician noise. To extract local features, the algorithm exploits signal similarities by processing similar patches as a group. An imperative reduction in the run time has been achieved as the CNN has been trained on a GPU with Convolutional Architecture for Fast Feature Embedding framework making it suitable for online reconstruction. The CNN based reconstruction also eliminates the necessity of optimization and prediction of noise level while denoising, which is the major advantage over existing state-of-the-art-techniques. Analytical experiments have been carried out with various undersampling schemes and the experimental results demonstrate high accuracy and consistent peak signal to noise ratio even at 20-fold undersampling. High undersampling rates provide scope for wireless transmission of k-space data and high speed reconstruction provides applicability of our algorithm for remote health monitoring.

3D MR image denoising using a modified adaptive high order singular value decomposition method.

Magnetic resonance (MR) images are generally degraded by random noise governed by Rician distributions. In this study, we developed a modified adaptive high order singular value decomposition (HOSVD) method, taking consideration of the nonlocal self-similarity and weighted Schatten p-norm. We extracted 3D cubes from noise images and classified the similar cubes by the Euclidean distance between cubes to construction a fourth-order tensor. Each rank of unfolding matrices was adaptively determined by weighted Schatten p-norm regularization. The latent noise-free 3D MR images can be obtained by an adaptive HOSVD. Denoising experiments were tested on both synthetic and clinical 3D MR images, and the results showed the proposed method outperformed several existing methods for Rician noise removal in 3D MR images.

Speckle and Rician Noise Removal from Medical Images and Ultrasound Images

In medical images, medical images are corrupted by different types of noise. It is important to get a precise picture and accurately observe the correspondence. Removing noise from medical images has become a very difficult problem in the field of the medical image. The most well-known noise reduction method, which is usually based on the local statistics of medical images, is efficient because of the noise reduction of medical images. In paper, an efficient and simple method for noise reduction from medical images is presented. The paper proposes a filtering system to combine both the Median filter and Gaussian filter to remove the Speckle noise form Medical and Ultrasound images. The image quality is measured through statistical quantities: Peak signal to noise ratio (PSNR). Experimental results show that the proposed system removes Speckle noise from medical images.

Adaptive fuzzy hexagonal bilateral filter for brain MRI denoising

Magnetic resonance image (MRI) plays a crucial role in medical applications for visual analysis and processing. Rician noise which arises from the MRI during acquisition can affect the quality of the image. This crucial issue should be addressed by denoising method. The proposed adaptive rician noise removal based on the bilateral filter using fuzzy hexagonal membership function improves the denoising efficiency at various noise variances and preserves the fine structures and edges. The fuzzy weights were obtained with the local mean (μi) and global mean (μg) by constructing hexagonal membership function for local order filter and bilateral filter. Bilateral filter is used to preserve the edges by smoothening the noises in MRI image and local filter is used to preserve the edges and retrieve the structural information. Brain MRI images are restored by multiplying its corresponding fuzzy weight with the restored image of local order filter and bilateral filter. Experiments on synthetic and clinical Brain MRI data were done at different noise levels by the proposed method and the existing methods. The result shows that the proposed method restores the image in better visual quality and can be well utilized for the diagnostic purpose at both low and high densities of rician noise.

MRI denoising by NeighShrink based on chi-square unbiased risk estimation.

NeighShrink is an efficient image denoising algorithm for the reduction of additive white Gaussian noise. However, it does not perform well in terms of Rician noise removal for MRI (Magnetic Resonance Imaging). Allowing for the characteristics of squared-magnitude MR (Magnetic Resonance) images, which follow a non-central chi-square distribution, the CURE (Chi-Square Unbiased Risk Estimation) is used to determine an optimal threshold for NeighShrink. Therefore, we propose the NeighShrinkCURE denoising algorithm. Bilateral filtering and cycle spinning are used to further improve denoising performance. Experimental results show that the proposed algorithm is simple and efficient, and provides good noise reduction while preserving edges and details well. Compared with some similar MRI denoising algorithms, the proposed algorithm has improvements in PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Although running time of the proposed algorithm has some increment compared with some current MRI denoising algorithms, the comprehensive performance of the proposed algorithm is superior to several current MRI denoising algorithms.