共查询到20条相似文献,搜索用时 24 毫秒
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Generalized total variation-based MRI Rician denoising model with spatially adaptive regularization parameters 总被引:1,自引:0,他引:1
Ryan Wen Liu Lin Shi Wenhua Huang Jing Xu Simon Chun Ho Yu Defeng Wang 《Magnetic resonance imaging》2014
Magnetic resonance imaging (MRI) is an outstanding medical imaging modality but the quality often suffers from noise pollution during image acquisition and transmission. The purpose of this study is to enhance image quality using feature-preserving denoising method. In current literature, most existing MRI denoising methods did not simultaneously take the global image prior and local image features into account. The denoising method proposed in this paper is implemented based on an assumption of spatially varying Rician noise map. A two-step wavelet-domain estimation method is developed to extract the noise map. Following a Bayesian modeling approach, a generalized total variation-based MRI denoising model is proposed based on global hyper-Laplacian prior and Rician noise assumption. The proposed model has the properties of backward diffusion in local normal directions and forward diffusion in local tangent directions. To further improve the denoising performance, a local variance estimator-based method is introduced to calculate the spatially adaptive regularization parameters related to local image features and spatially varying noise map. The main benefit of the proposed method is that it takes full advantage of the global MR image prior and local image features. Numerous experiments have been conducted on both synthetic and real MR data sets to compare our proposed model with some state-of-the-art denoising methods. The experimental results have demonstrated the superior performance of our proposed model in terms of quantitative and qualitative image quality evaluations. 相似文献
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混沌信号协同滤波去噪算法充分利用了混沌信号的自相似结构特征,具有良好的信噪比提升性能.针对该算法的滤波参数优化问题,考虑到最优滤波参数的选取受到信号特征、采样频率和噪声水平的影响,为提高该算法的自适应性使其更符合实际应用需求,基于排列熵提出一种滤波参数自动优化准则.依据不同噪声水平的混沌信号排列熵的不同,首先选取不同滤波参数对含噪混沌信号进行去噪,然后计算各滤波参数对应重构信号的排列熵,最后通过比较各重构信号的排列熵,选取排列熵最小的重构信号对应的滤波参数为最优滤波参数,实现滤波参数的优化.分析了不同信号特征、采样频率和噪声水平情况下滤波参数的选取规律.仿真结果表明,该参数优化准则能在不同条件下对滤波参数进行有效的自动最优化,提高了混沌信号协同滤波去噪算法的自适应性. 相似文献
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To improve the accuracy of structural and architectural characterization of living tissue with diffusion tensor imaging, an efficient smoothing algorithm is presented for reducing noise in diffusion tensor images. The algorithm is based on anisotropic diffusion filtering, which allows both image detail preservation and noise reduction. However, traditional numerical schemes for anisotropic filtering have the drawback of inefficiency and inaccuracy due to their poor stability and first order time accuracy. To address this, an unconditionally stable and second order time accuracy semi-implicit Craig-Sneyd scheme is adapted in our anisotropic filtering. By using large step size, unconditional stability allows this scheme to take much fewer iterations and thus less computation time than the explicit scheme to achieve a certain degree of smoothing. Second-order time accuracy makes the algorithm reduce noise more effectively than a first order scheme with the same total iteration time. Both the efficiency and effectiveness are quantitatively evaluated based on synthetic and in vivo human brain diffusion tensor images, and these tests demonstrate that our algorithm is an efficient and effective tool for denoising diffusion tensor images. 相似文献
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This paper presents an LMMSE-based method for the three-dimensional (3D) denoising of MR images assuming a Rician noise model. Conventionally, the LMMSE method estimates the noise-less signal values using the observed MR data samples within local neighborhoods. This is not an efficient procedure to deal with this issue while the 3D MR data intrinsically includes many similar samples that can be used to improve the estimation results. To overcome this problem, we model MR data as random fields and establish a principled way which is capable of choosing the samples not only from a local neighborhood but also from a large portion of the given data. To follow the similar samples within the MR data, an effective similarity measure based on the local statistical moments of images is presented. The parameters of the proposed filter are automatically chosen from the estimated local signal-to-noise ratio. To further enhance the denoising performance, a recursive version of the introduced approach is also addressed. The proposed filter is compared with related state-of-the-art filters using both synthetic and real MR datasets. The experimental results demonstrate the superior performance of our proposal in removing the noise and preserving the anatomical structures of MR images. 相似文献
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Non-local means (NLM) filtering is an efficacious algorithm in image denoising which searches the similar neighborhoods and estimates the pixel by averaging these neighborhoods. Some internal parameters such as patch size, search window size and smoothing strength have serious effects on filtering performance. This paper proposes an improved version of NLM by using weak textured patches based single image noise estimation and two-stage NLM with adaptive smoothing parameter. Our proposed method firstly applies weak textured patches based noise estimation to achieve the noise level of input noisy image. Then relying on the estimated noise level, we apply the first stage NLM with adaptive smoothing parameter to attain a basic denoised image. After that, the basic denoised image is refined by the second stage of NLM with smaller smoothing strength. Our experimental results show that the proposed algorithm outperforms the NLM and some NLM recent variants both in visual quality and numerical measures. Additionally, the potential halo effect is almost eliminated in the result images produced by our proposed method. 相似文献
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Feature-preserved denoising is of great interest in medical image processing. This article presents a wavelet-based bilateral filtering scheme for noise reduction in magnetic resonance images. Undecimated wavelet transform is employed to provide effective representation of the noisy coefficients. Bilateral filtering of the approximate coefficients improves the denoising efficiency and effectively preserves the edge features. Denoising is done in the square magnitude domain, where the noise tends to be signal independent and is additive. The proposed method has been adapted specifically to Rician noise. The visual and the diagnostic quality of the denoised image is well preserved. The quantitative and the qualitative measures used as the quality metrics demonstrate the ability of the proposed method for noise suppression. 相似文献
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This paper proposes a Rician noise reduction method for magnetic resonance (MR) images. The proposed method is based on adaptive non-local mean and guided image filtering techniques. In the first phase, a guidance image is obtained from the noisy image through an adaptive non-local mean filter. Sobel operators are applied to compute the strength of edges which is further used to control the spread of the kernel in non-local mean filtering. In the second phase, the noisy and the guidance images are provided to the guided image filter as input to restore the noise-free image. The improved performance of the proposed method is investigated using the simulated and real data sets of MR images. Its performance is also compared with the previously proposed state-of-the art methods. Comparative analysis demonstrates the superiority of the proposed scheme over the existing approaches. 相似文献
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图像增强技术可以有效地突出图像中的有用信息,已广泛应用于多个领域.现有的图像增强算法往往无法应对自然图像中复杂的梯度分布,难以准确保持图像中前景与背景的边缘信息.为了改善输出图像的边界过平滑问题,本文提出了一个基于多引导滤波的图像增强算法.首先,设计了一个以滤波核为变量的通用图像优化模型,现有的联合滤波器可视为该模型的解;然后,依据集成学习的思想,将联合滤波器中的单幅引导图像扩展到多幅,以更好地利用引导图中的结构信息进而获得更好的输出结果,并给出了一个多幅引导图的来源途径;最后,对多幅输出图像进行平滑,在图像优化模型中加入正则化项,以确保由多引导滤波得到的不同滤波输出保持一致.实验结果表明,本文算法在抑制图像噪声的同时,可以更好地保留物体的边界信息,从而使图像的信噪比进一步提升. 相似文献
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In this paper we present a magnetic resonance imaging (MRI) technique that is based on multiplicative regularization. Instead of adding a regularizing objective function to a data fidelity term, we multiply by such a regularizing function. By following this approach, no regularization parameter needs to be determined for each new data set that is acquired. Reconstructions are obtained by iteratively updating the images using short-term conjugate gradient-type update formulas and Polak-Ribière update directions. We show that the algorithm can be used as an image reconstruction algorithm and as a denoising algorithm. We illustrate the performance of the algorithm on two-dimensional simulated low-field MR data that is corrupted by noise and on three-dimensional measured data obtained from a low-field MR scanner. Our reconstruction results show that the algorithm effectively suppresses noise and produces accurate reconstructions even for low-field MR signals with a low signal-to-noise ratio. 相似文献
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Magnetic Resonance (MR) image is often corrupted with a complex white Gaussian noise (Rician noise) which is signal dependent. Considering the special characteristics of Rician noise, we carry out nonlocal means denoising on squared magnitude images and compensate the introduced bias. In this paper, we propose an algorithm which not only preserves the edges and fine structures but also performs efficient denoising. For this purpose we have used a Laplacian of Gaussian (LoG) filter in conjunction with a nonlocal means filter (NLM). Further, to enhance the edges and to accelerate the filtering process, only a few similar patches have been preselected on the basis of closeness in edge and inverted mean values. Experiments have been conducted on both simulated and clinical data sets. The qualitative and quantitative measures demonstrate the efficacy of the proposed method. 相似文献
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Superiority of 3D wavelet-packet denoising in MR microscopy 总被引:1,自引:0,他引:1
Ghugre NR Martin M Scadeng M Ruffins S Hiltner T Pautler R Waters C Readhead C Jacobs R Wood JC 《Magnetic resonance imaging》2003,21(8):913-921
Three dimensional Magnetic Resonance Imaging (MRI) datasets are becoming increasingly important in clinical and research applications because of their inherent signal to noise (SNR) advantages, high resolution and isotropic voxels. Despite SNR advantages, some 3D acquisitions may be SNR-limited, particularly in MR microscopy. Historically, both classic filtering and wavelet-based denoising techniques have been performed on a slice-by-slice basis. In principle, adaptive techniques such as best- basis wavelet-packet denoising might offer inherent advantages when performed in 3D, instead of 2D, by tracking through plane "structure" and suppressing noise "pseudostructure." This hypothesis was tested in 10 volumetric MR microscopy datasets from several different MR microscopy atlas projects. 3D wavelet-packet denoised images consistently yielded lower minimum mean-square error and subjectively perceived noise power than corresponding 2D denoised images using otherwise identical algorithms and parameters. MR microscopy researchers preferred the denoised images to the unprocessed images for their atlas projects. 相似文献
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In recent years, there has been a concerted effort to reduce the MR scan time. Signal processing research aims at reducing the scan time by acquiring less K-space data. The image is reconstructed from the subsampled K-space data by employing compressed sensing (CS)-based reconstruction techniques. In this article, we propose an alternative approach to CS-based reconstruction. The proposed approach exploits the rank deficiency of the MR images to reconstruct the image. This requires minimizing the rank of the image matrix subject to data constraints, which is unfortunately a nondeterministic polynomial time (NP) hard problem. Therefore we propose to replace the NP hard rank minimization problem by its nonconvex surrogate — Schatten p-norm minimization. The same approach can be used for denoising MR images as well.Since there is no algorithm to solve the Schatten p-norm minimization problem, we derive an efficient first-order algorithm. Experiments on MR brain scans show that the reconstruction and denoising accuracy from our method is at par with that of CS-based methods. Our proposed method is considerably faster than CS-based methods. 相似文献
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扫描电镜能直观观察样品的表面结构,但其高分辨形貌成像图固有的噪声不利于图像分析。针对集成电路器件扫描电镜成像图的去噪声问题,采用了通过滑动条方式自适应设置图像二值化阈值,将数学形态学处理方法与图像二值化相结合,实现了对图像噪声的自动去除处理;同时还设计了通过手动勾勒图像中的多边形区域实现去除噪声的功能;为使图像达到更好的效果,系统还可允许针对自动去噪后的图像自行选择是否进行手动去噪,并设计实现了风格直观简洁,易于操作的交互式用户界面。对多幅集成电路器件扫描电镜成像图进行去噪声处理的结果和对去噪前后的图像进行无参考图像质量评价的数据表明,该方法有效地改善了扫描电镜图的信噪比,获得了突出前景等有用信息。 相似文献
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