Despeckling low SNR, low contrast ultrasound images via anisotropic level set diffusion

Speckle is a form of multiplicative and locally correlated noise which degrades the signal-to-noise ratio (SNR) and contrast resolution of ultrasound images. This paper presents a new anisotropic level set method for despeckling low SNR, low contrast ultrasound images. The coefficient of variation, a speckle-robust edge detector is embedded in the well known geodesic “snakes” model to smooth the image level sets, while preserving and sharpening edges of a speckled image. The method achieves much better speckle suppression and edge preservation compared to the traditional anisotropic diffusion based despeckling filters. In addition, the performance of the filter is less sensitive to the speckle scale of the image and edge contrast parameter, which makes it more suitable for the detection of low contrast features in an ultrasound image. We validate the method using both synthetic and real ultrasound images and quantify the performance improvement over other state-of-the-art algorithms in terms of speckle noise reduction and edge preservation indices.     

Cognition and removal of impulse noise with uncertainty

Uncertainties are the major inherent feature of impulse noise. This fact makes image denoising a difficult task. Understanding the uncertainties can improve the performance of image denoising. This paper presents a novel adaptive detail-preserving filter based on the cloud model (CM) to remove impulse noise. It is called the CM filter. First, an uncertainty-based detector identifies the pixels corrupted by impulse noise. Then, a weighted fuzzy mean filter is applied to remove the noise candidates. The experimental results show that, compared with the traditional switching filters, the CM filter makes a great improvement in image denoising. Even at a noise level as high as 95%, the CM filter still can restore the image with good detail preservation.     

Smoothing low-SNR molecular images via anisotropic median-diffusion

We propose a new anisotropic diffusion filter for denoising low-signal-to-noise molecular images. This filter, which incorporates a median filter into the diffusion steps, is called an anisotropic median-diffusion filter. This hybrid filter achieved much better noise suppression with minimum edge blurring compared with the original anisotropic diffusion filter when it was tested on an image created based on a molecular image model. The universal quality index, proposed in this paper to measure the effectiveness of denoising, suggests that the anisotropic median-diffusion filter can retain adherence to the original image intensities and contrasts better than other filters. In addition, the performance of the filter is less sensitive to the selection of the image gradient threshold during diffusion, thus making automatic image denoising easier than with the original anisotropic diffusion filter. The anisotropic median-diffusion filter also achieved good denoising results on a piecewise-smooth natural image and real Raman molecular images.     

基于同态滤波和全变差的激光图像降噪方法

针对激光主动成像图像的特点,提出了一种基于同态滤波和全变差的图像降噪方法.首先对图像进行同态滤波,提高图像的对比度并去除激光图像的散斑噪声,然后采用基于最小化全变差模型去除激光图像的高斯噪声和脉冲噪声.采用信噪比、对比度和亮度失真度作为图像降噪效果的评估,将算法与中值降噪、小波与中值滤波结合降噪等进行对比实验,实验结果...     

水下二维及三维距离选通成像去噪技术研究

相比传统水下摄像机,水下距离选通成像的作用距离可提高两三倍,同时基于该技术可实现快速高分辨率三维成像,在水底详查、避障导航、海洋科学研究、矿藏开发等方面具有广泛应用前景。虽然距离选通成像可通过空间切片的方式抑制水体的后向散射噪声,实现感兴趣区较高质量的成像,但是,在选通图像中仍不可避免地存在空间切片水体的后向散射噪声,导致图像信噪比和对比度降低,尤其是对于远距离目标或低反射率目标。介绍了针对水下距离选通二维成像及三维成像去噪方面的技术研究。在二维成像方面,一是采用双平台自适应增强算法提高图像对比度,更好地满足人眼视觉要求;二是采用参考水体去噪算法实现含目标图像的水体去噪,提高信噪比。在三维成像方面,针对距离能量相关三维成像,一是利用参考水体去噪算法实现去噪三维重建,二是采用双边滤波法对三维图像中数据空洞进行修复,提高三维图像质量。所述四种方法可独立或联合用于水下距离选通成像的去噪增强,提高水下距离选通成像技术的性能。     

基于改进三维块匹配滤波的红外全息降噪算法北大核心CSCD

红外全息技术更适用于长距离的大视场成像,但其散斑噪声与高斯噪声对图像质量的影响也更加显著,限制了红外全息技术的应用与推广。本文通过引入全局傅立叶阈值与自适应维纳滤波的方法对三维块匹配滤波算法进行优化,提高了其对红外全息图像降噪的适应性与细节保留,得到改进的三维块匹配滤波算法,并与多种采用传统滤波方法的结果进行了对比。结果表明,改进后的算法可以在对红外全息图像中的高斯噪声等环境噪声与散斑噪声进行降噪的同时保留更多细节,是一种更加适用于红外全息图像的降噪方法。     

Patch based image denoising using the finite ridgelet transform for less artifacts

Patch based denoising methods have proved to lead to state-of-the-art results. However, in contrast with intensive pursuing of higher peak signal to noise ratio (PSNR), less attention is paid to visual quality improvement of denoised images. In this paper, we first compare the denoising performance in edge and smooth regions. Results reveal that edge regions are the main source for potential performance improvement. This motivates us to investigate the use of the finite ridgelet transform as a local transform for better preservation of directional singularities. A two stage denoising algorithm is then proposed to improve the representation of detail structures. Experimental results in denoising images which only contain white noise show that the proposed algorithm consistently outperforms other methods in terms of PSNR and Structural SIMilarity index. Denoised images by the proposed method also demonstrate good visual quality with the least artifacts and fake structures in experiments on natural images.     

Nonlinear multiscale wavelet diffusion for speckle suppression and edge enhancement in ultrasound images

This paper introduces a novel nonlinear multiscale wavelet diffusion method for ultrasound speckle suppression and edge enhancement. This method is designed to utilize the favorable denoising properties of two frequently used techniques: the sparsity and multiresolution properties of the wavelet, and the iterative edge enhancement feature of nonlinear diffusion. With fully exploited knowledge of speckle image models, the edges of images are detected using normalized wavelet modulus. Relying on this feature, both the envelope-detected speckle image and the log-compressed ultrasonic image can be directly processed by the algorithm without need for additional preprocessing. Speckle is suppressed by employing the iterative multiscale diffusion on the wavelet coefficients. With a tuning diffusion threshold strategy, the proposed method can improve the image quality for both visualization and auto-segmentation applications. We validate our method using synthetic speckle images and real ultrasonic images. Performance improvement over other despeckling filters is quantified in terms of noise suppression and edge preservation indices.     

基于提升小波变换和中值滤波的图像去噪方法研究

针对实际拍摄的背景复杂、目标对比度和信噪比低的图像,在综合考滤图像去噪平滑效果、图像清晰程度和时间复杂度的基础上,提出一种基于提升小波变换和中值滤波的图像去噪方法.首先对含噪图像进行提升小波分解,再在图像高频部分进行中值滤波以改善图像的消噪效果,最后采用信噪比(SNR)与均方根误差(RMSE)和图像灰度曲面图作为图像去噪效果的评估,将提升小波变换和中值滤波相结合的图像去噪方法与小波去噪、小波与中值滤波结合消噪等进行对比实验.实验结果表明,该方法既能消除图像噪声又能达到保持其图像边缘要求,且时间度较低.     

Impact of applying pre-processing techniques for improving classification accuracy

Image denoising is a procedure aimed at removing noise from images while retaining as many important signal features as possible. Many images suffer from poor contrast due to inadequate illumination or finite sensitivity of the imaging device, electronic sensor noise or atmospheric disturbances. This paper proposes a hybrid directional lifting technique for image denoising to retain the original information present in the images. The primary objective of this paper is to show the impact of applying preprocessing techniques for improving classification accuracy. In order to classify the image accurately, effective preservation of edges and contour details of an image is essential. The discrete wavelet transform-based interpolation technique is developed for resolution enhancement. The image is then classified using support vector machine classifier, which is well suitable for image classification. The efficiency of the classifier is analyzed based on receiver operating characteristic (ROC) curves. The quantitative performance measures peak signal to noise ratio and ROC analysis show the significance of the proposed techniques.     

采用非局部均值的连续太赫兹图像去噪处理

太赫兹辐射能够穿透大多数对可见光和近红外光不透明的物质。提高成像质量是成像系统的关键,尤其对探测器性能较低的面阵成像更为重要。通过数字图像处理方法改善成像质量是一条重要的解决途径。应用非局部均值(NLM)分别对真实的透射扫描、反射扫描和透射面阵、反射面阵的太赫兹图像进行了去噪处理,选取不同的控制参数进行了对比分析,同时对比了均值滤波处理结果。实验结果表明:非局部均值能够较好地去除连续太赫兹图像噪声、提高成像质量,对噪声严重的面阵成像去噪效果最明显。非局部均值去噪并保持边缘能力明显好于均值滤波。     

曲率差分驱动的极小曲面滤波器

为提高全变分图像降噪模型的降噪性能和边缘保持性能,该文提出一种曲率差分驱动的极小曲面滤波器。首先,在平均曲率滤波器模型基础上,引入自适应曲率差分边缘探测函数,建立曲率差分驱动的极小曲面滤波器模型;接着,从微分几何理论角度,阐述该能量泛函模型的物理意义和平均曲率能量减小方法;最后,在离散的图像域,通过迭代的方式使图像每个像素邻域内的曲面向极小曲面迭代进化,实现能量泛函的平均曲率能量极小化,从而能量泛函的总能量也完成极小化。实验表明,该滤波器不仅能去除高斯噪声、椒盐噪声,还能去除这两类噪声构成的混合噪声,其降噪性能和边缘保持性能优于同类型的其他5种全变分算法。     

基于LED辅助照明的水下图像增强算法研究

水下成像环境十分复杂,在成像过程中受到多种因素的影响,如光照的非均匀性,水对光的后向散射,成像器件的光学噪声,导致图像对比度低,细节模糊.针对水下图像的特点,提出运用同态滤波和双正交小波阈值滤波法相结合的图像增强算法改善水下图像光照的非均匀性、滤除噪声、提高水下图像的对比度,并通过峰值信噪比、均方差对该算法进行了性能评价.     

自适应维纳滤波在钢水红外图像去噪中的应用

红外测温系统的应用减少了人工测温的安全事故,但其温度的准确性取决于由红外热像仪获得的图像的质量.为了对钢水红外图像质量的影响,提出了基于自适应维纳滤波的去噪方法.通过自相关的参数指数衰减模型来控制算法的计算复杂性和敏感性,进而有效提高维纳滤波器的去降噪性能.基于对不同温度下钢水红外图像的去噪处理,验证了所提去噪方法比维...     

基于稀疏表示的红外图像去噪算法研究

红外图像具有动态范围窄、对比度低、易受噪声污染等缺点,传统红外图像去噪算法在去除噪声的同时也滤掉了图像细节。提出了一种基于稀疏表示的红外图像去噪新方法。该方法首先将原始红外图像进行聚类分析,再将每一聚类子图像分解成字典,由稀疏系数矩阵重构去噪后的红外图像。实验结果表明,该方法相比于传统红外图像去噪算法,能更好地保留图像的细节信息,视觉效果比较理想。     

一种改进的小波阈值裂纹图像去噪法

文中采用一种新的小波阈值法进行数字图像去噪。该方法是在原有小波算法之上进行改进,一方面改进闽值公式,寻求最优的阈值,获取更好的边缘保护,从而达到图像原始信息完整性;另一方面通过优化阁值函数获得改进的阚值方法,提高小波系数阈值判断的准确性,获得更好的去噪效果。实验结果表明,与通常的方法相比较,能更好的去除数字图像噪声、保护图像边缘,获得更好的峰值信噪比、边缘保持指数和更好的视觉效果,达到去噪要求。     

基于曲面特性的图像非线性扩散滤波

利用扩散滤波进行图像降噪的过程中,一个核心问题是,如何控制扩散系数,使得模型在图像信息位置停止扩散,而在噪声处有效地扩散。为了更好地解决此问题,本文采用了一种新的思想,把图像看作是三维空间的一个曲面,这样可以得到图像曲面的两个基本特性:高斯曲率和平均曲率。为了能够在图像进行扩散滤波处理中有效地利用图像在三维空间中的这些曲面特性,文章分析了已有的基于平均曲率或高斯曲率的非线性扩散滤波模型,总结了平均曲率和高斯曲率的特点,并在此基础上,提出了基于混合曲率的扩散滤波模型;该模型作为一种新的基于曲面特性的图像扩散滤波模型,同时利用了图像的高斯曲率和平均曲率,恰当地融合了两种曲率的特点,能够以相对较快的速度滤除噪声,同时保持图像的细节特征。      

眼轴及眼前节SS-OCT一体化成像系统

针对现有眼轴测量仪器无法同时实现大量程眼轴测量及眼前节成像的功能,设计了眼轴和眼前节同步测量的大量程扫频光学相干层析成像系统。该系统通过设计大量程扫频光源增加成像深度,并在外部设计搭建标定光路进行k域重采样以提高大量程干涉范围内的成像质量,从而在获取高精度眼轴信息的同时对眼前节成像;针对该系统OCT图像信噪比较低的问题,文中采用基于各向异性滤波的保边去噪算法对图像中的散斑噪声进行抑制以提高图像对比度。系统分别使用等比例人眼模型和离体鱼眼进行成像实验,实验结果表明:该系统成像深度可达到69 mm,在精准测量眼轴纵向五层结构的同时完成12 mm范围内的眼前节横向扫描,眼轴长度平均测量误差0.04 mm,成像时间约为0.45 s,满足临床医学应用中实时性的要求。     

An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images

A critical issue in image restoration is the problem of noise removal while keeping the integrity of relevant image information. Denoising is a crucial step to increase image quality and to improve the performance of all the tasks needed for quantitative imaging analysis. The method proposed in this paper is based on a 3-D optimized blockwise version of the nonlocal (NL)-means filter (Buades, , 2005). The NL-means filter uses the redundancy of information in the image under study to remove the noise. The performance of the NL-means filter has been already demonstrated for 2-D images, but reducing the computational burden is a critical aspect to extend the method to 3-D images. To overcome this problem, we propose improvements to reduce the computational complexity. These different improvements allow to drastically divide the computational time while preserving the performances of the NL-means filter. A fully automated and optimized version of the NL-means filter is then presented. Our contributions to the NL-means filter are: 1) an automatic tuning of the smoothing parameter; 2) a selection of the most relevant voxels; 3) a blockwise implementation; and 4) a parallelized computation. Quantitative validation was carried out on synthetic datasets generated with BrainWeb (Collins, , 1998). The results show that our optimized NL-means filter outperforms the classical implementation of the NL-means filter, as well as two other classical denoising methods [anisotropic diffusion (Perona and Malik, 1990)] and total variation minimization process (Rudin, , 1992) in terms of accuracy (measured by the peak signal-to-noise ratio) with low computation time. Finally, qualitative results on real data are presented.     

Evaluation of the adaptive speckle suppression filter for coronary optical coherence tomography imaging

During the last few years, optical coherence tomography (OCT) has demonstrated considerable promise as a method of high-resolution intravascular imaging. The goal of this study was to apply and to test the applicability of the rotating kernel transformation (RKT) technique to the speckle reduction and enhancement of OCT images. The technique is locally adaptive. It is based on sequential application of directional masks and selection of the maximum of all outputs. This method enhances the image features by emphasizing thin edges while suppressing a noisy background. Qualitatively, the RKT algorithm provides noticeable improvement over the original image. All processed images are smoother and have better-defined borders of media, intima, and plaque. The quantitative evaluation of RKT performance showed that in terms of average contrast-to-noise ratio, there is a significant improvement in image quality between original and enhanced images. The RKT image enhancement technique shows great promise in improving OCT images for superior boundary identification.