Wavelet-based feature extraction from oceanographic images

Features in satellite images of the oceans often have weak edges. These images also have a significant amount of noise, which is either due to the clouds or atmospheric humidity. The presence of noise compounds the problems associated with the detection of features, as the use of any traditional noise removal technique will also result in the removal of weak edges. Recently, there have been rapid advances in image processing as a result of the development of the mathematical theory of wavelet transforms. This theory led to multifrequency channel decomposition of images, which further led to the evolution of important algorithms for the reconstruction of images at various resolutions from the decompositions. The possibility of analyzing images at various resolutions can be useful not only in the suppression of noise, but also in the detection of fine features and their classification. This paper presents a new computational scheme based on multiresolution decomposition for extracting the features of interest from the oceanographic images by suppressing the noise. The multiresolution analysis from the median presented by Starck-Murtagh-Bijaoui (1994) is used for the noise suppression     

An algorithm for noise suppression in dual energy CT material density images

Dual-energy material density images obtained by prereconstruction-basis material decomposition techniques offer specific tissue information, but they exhibit relatively high pixel noise. It is shown that noise in the material density images is negatively correlated and that this can be exploited for noise reduction in the two-basis material density images. The algorithm minimizes noise-related differences between pixels and their local mean values, with the constraint that monoenergetic CT values, which can be calculated from the density images, remain unchanged. Applied to the material density images, a noise reduction by factors of 2 to 5 is achieved. While quantitative results for regions of interest remain unchanged, edge effects can occur in the processed images. To suppress these, locally adaptive algorithms are presented and discussed. Results are documented by both phantom measurements and clinical examples.     

低秩遮挡图像去噪方法

为获得清晰的低秩图像,提出一种将低秩矩阵填充 (LRMC)与低秩矩阵恢 复(LRMR)联合的新模型,基于非精确增广拉格朗日乘子(IALM)法进行求解,运用LRMC去除遮 挡并填充缺失部 分,再利用LRMR去除噪声,得到完整的图像。以恢复时间、信噪比(SNR)、峰 值信噪比(PSNR)、差错率(err)等做评价标准,对3幅受噪声污染的图像的恢复结果表明, 本文提出的联合LRMC与LRMR的新模型,既能去除遮挡又能够填充图像的缺失部 分,能够达到理想的恢复效果。     

声呐图像中的混合噪声抑制方法

针对含有混合噪声的声呐图像,提出了中值滤波与形态学滤波的组合算法,并对常用的图像滤波算法以及算法组合进行了定量分析比较,仿真结果表明,中值滤波和形态学滤波的组合算法对于声呐图像中混合噪声的滤除是较为理想的.     

Nouvelle méthode de restauration d’images photographiques en couleur dégradées par le bruit de la transformation RGB→YCbCr

This paper presents a new method of restoration of photographic color images corrupted by the noise of transformation RGB→YCbCr. This transformation introduces into certain images a high frequency type noise. This noise is highlighted and corrected. The correction is made by attenuation of the coefficients Y, Cb and Cr followed by their rebuilding by multiplicative factors, which come from transformation relations analysis of spaces RGB and YCbCr. A comparative study with the Wiener’s filter shows its effectiveness to restore images corrupted even by other noises like the black caused by an insufficiency light during snapping process and its superiority on the median filter as for the correction of this noise.     

图像边缘检测方法分析与研究

边缘是图像的重要特征,边缘检测在计算机视觉、图像分析等应用中起着重要作用,是图像目标检测中一个基础而又困难的问题,本文分析了常规的边缘检测方法及其特点,并用这些方法分别对原始图像和噪声图像进行了处理,处理的结果表明,Sobel、Roberts、Prewitt、Kirsch、LOG算子的图像处理效果各有利弊,它们在定位精度、噪声敏感度和复杂度之间存在互相抑制的关系。     

Fast and robust multiframe super resolution

Super-resolution reconstruction produces one or a set of high-resolution images from a set of low-resolution images. In the last two decades, a variety of super-resolution methods have been proposed. These methods are usually very sensitive to their assumed model of data and noise, which limits their utility. This paper reviews some of these methods and addresses their short-comings. We propose an alternate approach using L1 norm minimization and robust regularization based on a bilateral prior to deal with different data and noise models. This computationally inexpensive method is robust to errors in motion and blur estimation and results in images with sharp edges. Simulation results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods.     

A universal noise removal algorithm with an impulse detector.

We introduce a local image statistic for identifying noise pixels in images corrupted with impulse noise of random values. The statistical values quantify how different in intensity the particular pixels are from their most similar neighbors. We continue to demonstrate how this statistic may be incorporated into a filter designed to remove additive Gaussian noise. The result is a new filter capable of reducing both Gaussian and impulse noises from noisy images effectively, which performs remarkably well, both in terms of quantitative measures of signal restoration and qualitative judgements of image quality. Our approach is extended to automatically remove any mix of Gaussian and impulse noise.     

散斑噪声抑制算法比较研究

文章综述了散斑噪声抑制算法的发展，并且通过实际的激光雷达图像处理比较了它们的图像的边缘保持能力，还计算了其算法处理图像的散斑指数，比较了它们的散班抑制能力。     

A Robust Hidden Markov Gauss Mixture Vector Quantizer for a Noisy Source

Noise is ubiquitous in real life and changes image acquisition, communication, and processing characteristics in an uncontrolled manner. Gaussian noise and Salt and Pepper noise, in particular, are prevalent in noisy communication channels, camera and scanner sensors, and medical MRI images. It is not unusual for highly sophisticated image processing algorithms developed for clean images to malfunction when used on noisy images. For example, hidden Markov Gauss mixture models (HMGMM) have been shown to perform well in image segmentation applications, but they are quite sensitive to image noise. We propose a modified HMGMM procedure specifically designed to improve performance in the presence of noise. The key feature of the proposed procedure is the adjustment of covariance matrices in Gauss mixture vector quantizer codebooks to minimize an overall minimum discrimination information distortion (MDI). In adjusting covariance matrices, we expand or shrink their elements based on the noisy image. While most results reported in the literature assume a particular noise type, we propose a framework without assuming particular noise characteristics. Without denoising the corrupted source, we apply our method directly to the segmentation of noisy sources. We apply the proposed procedure to the segmentation of aerial images with Salt and Pepper noise and with independent Gaussian noise, and we compare our results with those of the median filter restoration method and the blind deconvolution-based method, respectively. We show that our procedure has better performance than image restoration-based techniques and closely matches to the performance of HMGMM for clean images in terms of both visual segmentation results and error rate.     

CCD noise removal in digital images.

In this work, we propose a denoising scheme to restore images degraded by CCD noise. The CCD noise model, measured in the space of incident light values (light space), is a combination of signal-independent and signal-dependent noise terms. This model becomes more complex in image brightness space (normal camera output) due to the nonlinearity of the camera response function that transforms incoming data from light space to image space. We develop two adaptive restoration techniques, both accounting for this nonlinearity. One operates in light space, where the relationship between the incident light and light space values is linear, while the second method uses the transformed noise model to operate in image space. Both techniques apply multiple adaptive filters and merge their outputs to give the final restored image. Experimental results suggest that light space denoising is more efficient, since it enables the design of a simpler filter implementation. Results are given for real images with synthetic noise added, and for images with real noise.     

Using perturbation theory to compute the morphological similarity of diffusion tensors

Computing the morphological similarity of diffusion tensors (DTs) at neighboring voxels within a DT image, or at corresponding locations across different DT images, is a fundamental and ubiquitous operation in the postprocessing of DT images. The morphological similarity of DTs typically has been computed using either the principal directions (PDs) of DTs (i.e., the direction along which water molecules diffuse preferentially) or their tensor elements. Although comparing PDs allows the similarity of one morphological feature of DTs to be visualized directly in eigenspace, this method takes into account only a single eigenvector, and it is therefore sensitive to the presence of noise in the images that can introduce error intothe estimation of that vector. Although comparing tensor elements, rather than PDs, is comparatively more robust to the effects of noise, the individual elements of a given tensor do not directly reflect the diffusion properties of water molecules. We propose a measure for computing the morphological similarity of DTs that uses both their eigenvalues and eigenvectors, and that also accounts for the noise levels present in DT images. Our measure presupposes that DTs in a homogeneous region within or across DT images are random perturbations of one another in the presence of noise. The similarity values that are computed using our method are smooth (in the sense that small changes in eigenvalues and eigenvectors cause only small changes in similarity), and they are symmetric when differences in eigenvalues and eigenvectors are also symmetric. In addition, our method does not presuppose that the corresponding eigenvectors across two DTs have been identified accurately, an assumption that is problematic in the presence of noise. Because we compute the similarity between DTs using their eigenspace components, our similarity measure relates directly to both the magnitude and the direction of the diffusion of water molecules. The favorable performance characteristics of our measure offer the prospect of substantially improving additional postprocessing operations that are commonly performed on DTI datasets, such as image segmentation, fiber tracking, noise filtering, and spatial normalization.     

Bilateral filtering with adaptation to phase coherence and noise

In this paper, a bilateral filter with adaptive domain and range parameter is introduced for image denoising. Since the objective of denoising is to reduce noise as much as possible while preserving the perceptually important details, the parameters are adjusted in accordance with perceptual significance of pixels and noise level. The domain parameter is obtained by using the maximum and minimum moments of local phase coherence for being the representative of image details such as edges and corners of an image. The range parameter is estimated from the intensity-homogeneity measurements for their ability to represent the underlying noise. In addition, the filter is applied in an iterative manner to reduce the residual noise. Experiments are carried out using various standard images, and the results show that the proposed method is more effective in reducing additive white Gaussian noise as compared to several recently introduced denoising techniques in terms of the peak signal-to-noise ratio, structural similarity index and visual quality. In addition, experiments performed using real noisy images reveal the ability of the proposed filter to provide denoised images of better visual quality.     

Local maximum likelihood segmentation of echocardiographic images with Rayleigh distribution

In order to interpret ultrasound images, it is important to understand their formation and the properties that affect them, especially speckle noise. This image texture, or speckle, is a correlated and multiplicative noise that inherently occurs in all types of coherent imaging systems. Indeed, its statistics depend on the density and on the type of scatterers in the tissues. This paper presents a new method for echocardiographic images segmentation in a variational level set framework. A partial differential equation-based flow is designed locally in order to achieve a maximum likelihood segmentation of the region of interest. A Rayleigh probability distribution is considered to model the local B-mode ultrasound images intensities. In order to confront more the speckle noise and local changes of intensity, the proposed local region term is combined with a local phase-based geodesic active contours term. Comparison results on natural and simulated images show that the proposed model is robust to attenuations and captures well the low-contrast boundaries.     

基于局部熵的量子衍生医学超声图像去斑

针对现有医学超声图像去斑方法的不足,该文提出一种基于局部熵的量子衍生医学超声图像去斑新方法。首先,将对数变换后的图像进行双树复小波变换(DTCWT),并对信号与噪声分别建模;然后,提取复小波中子代与父代小波系数的实部,计算其局部熵并进行归一化乘积,结合量子衍生理论得到用来调整信号与噪声出现概率的可调参数;最后,利用改进的双变量收缩函数获得去斑后的图像。通过实验,结果表明该方法与已有方法相比能够更有效地滤除医学超声图像中的斑点噪声并保留细节信息。     

印制电路板光电图像边缘信息提取

针对印制电路板(printed circuit board, PCB)光电图像模糊且含噪声的具体情况,提出了改进的边缘信息提取算法。首先分别对自适应模糊集增强算法与数学形态学边缘检测算法(edge detection algorithm of mathematical morphology, EDAMM)实施改进,并分析了其基本原理。然后结合这两种算法对PCB光电图像进行预处理及边缘信息提取。最后对两幅由不同成像系统获取的PCB光电图像进行了边缘信息提取实验。结果表明：用本文算法获得的PCB光电图像明暗对比度较高,并提取了精确且清晰的图像边缘信息,明显减少了噪声,所得图像的优质系数较高,两幅图像的优质系数分别是0.885 2、0.874 9,均高于本文中所提到的另外4种算法的结果。可见,采用本文算法可以更好地去除PCB光电图像中的模糊与噪声,并精确地提取出PCB光电图像的边缘信息。     

小波变换在微光图像消噪中的应用算法研究

根据小波变换用于图像消噪的原理,结合微光图像噪声的闪烁颗粒性特点,对小波变换用于微光图像消噪时的小波基及小波分解层次的选取进行了分析,得出采用Haar小波进行一层分解即可满足微光图像消噪要求的结论。为了选取小波消噪的系数阈值,通过对三幅微光图像小波系数的直方图分析,设计了阈值选取算法,并针对微光图像,得出了消噪的经验阈值。经过仿真实验及算法复杂度的时间分析,在实时性和微光图像消噪效果之间取得了平衡。     

基于噪声检测的高密度椒盐噪声自适应滤波算法

为了减少图像中的椒盐噪声对后续图像处理的影响,针对高密度噪声污染图像,提出了基于噪声检测的高密度椒盐噪声滤波算法。噪声检测方法理论可靠,保证了较高的噪声检测率,根据噪声点邻域信号点分布的不同采用不同的策略,能最大限度的保护图像的细节信息,使得高密度噪声污染图像也能得到较好地恢复。实验结果表明,所提出的滤波算法具有较强的自适应性、较高的算法保真率及较好的滤波效果。     

Ramp-preserving denoising for conductivity image reconstruction in magnetic resonance electrical impedance tomography

In magnetic resonance electrical impedance tomography, among several conductivity image reconstruction algorithms, the harmonic B(z) algorithm has been successfully applied to B(z) data from phantoms and animals. The algorithm is, however, sensitive to measurement noise in B(z) data. Especially, in in vivo animal and human experiments where injection current amplitudes are limited within a few milliampere at most, measured B(z) data tend to have a low SNR. In addition, magnetic resonance (MR) signal void in outer layers of bones and gas-filled organs, for example, produces salt-pepper noise in the MR phase and, consequently, B(z) images. The B(z) images typically present areas of sloped transitions, which can be assimilated to ramps. Conductivity contrasts change ramp slopes in B(z) images and it is critical to preserve positions of those ramps to correctly recover edges in conductivity images. In this paper, we propose a ramp-preserving denoising method utilizing a structure tensor. Using an eigenvalue analysis, we identified local regions of salt-pepper noise. Outside the identified local regions, we applied an anisotropic smoothing to reduce noise while preserving their ramp structures. Inside the local regions of salt-pepper noise, we used an isotropic smoothing. After validating the proposed denoising method through numerical simulations, we applied it to in vivo animal imaging experiments. Both numerical simulation and experimental results show significant improvements in the quality of reconstructed conductivity images.