SAR image processing using adaptive stack filter

Stack filters are a special case of non-linear filters. They have a good performance for filtering images with different types of noise while preserving edges and details. A stack filter decomposes an input image into several binary images according to a set of thresholds. Each binary image is filtered by a Boolean function. The Boolean function that characterizes an adaptive stack filter is optimal and is computed from a pair of images consisting of an ideal noiseless image and its noisy version. In this work the behavior of adaptive stack filters on synthetic aperture radar (SAR) data is evaluated. With this aim, the equivalent number of looks for stack filtered data are calculated to assess the speckle noise reduction capability of this filter. Then a classification of simulated and real SAR images is carried out on data filtered with a stack filter trained with selected samples. The results of a maximum likelihood classification of these data are evaluated and compared with the results of classifying images previously filtered using the Lee and the Frost filters.     

Cover ERS-1/2 SAR monitoring of dangerous ice phenomena along the western part of Northern Sea Route

A model that compensates for misregistration effects on change detection results shows promise for reducing artefacts and enhancing land change features at or near the pixel scale and for reducing noise caused by misregistered muti-temporal images. Sparse estimates of misregistration across the scene are combinedwithcalculations of spatial brightness gradients toadjust the magnitude of multi-temporal image differences. The model is tested on a multi-temporal Landsat Thematic Mapper image data set for a rapidly urbanizing landscape in southern California.     

A model for radar images and its application to adaptive digital filtering of multiplicative noise

Standard image processing techniques which are used to enhance noncoherent optically produced images are not applicable to radar images due to the coherent nature of the radar imaging process. A model for the radar imaging process is derived in this paper and a method for smoothing noisy radar images is also presented. The imaging model shows that the radar image is corrupted by multiplicative noise. The model leads to the functional form of an optimum (minimum MSE) filter for smoothing radar images. By using locally estimated parameter values the filter is made adaptive so that it provides minimum MSE estimates inside homogeneous areas of an image while preserving the edge structure. It is shown that the filter can be easily implemented in the spatial domain and is computationally efficient. The performance of the adaptive filter is compared (qualitatively and quantitatively) with several standard filters using real and simulated radar images.     

Analysis of spatial distribution pattern of change-detection error caused by misregistration

Misregistration between multitemporal remotely sensed images is one of the significant sources of change-detection errors. In this study, spatial distribution of change-detection errors induced by misregistration was analysed quantitatively. First, multitemporal images are registered with different misregistration values measured by root mean square error (RMSE) from 0 to 1 pixels. The image differencing method, one of the most widely used change-detection methods, is then used to detect changes. Finally, the spatial distribution pattern of change-detection errors caused by misregistration is analysed using buffering analysis based on multitemporal image edges. Experimental results indicate that the commission errors caused by misregistration values from 0 to 1 pixels are almost always within 1 pixel of the edge, regardless of image resolution. In addition, the omission errors falling within 1 pixel of the edges are about 70% for medium-resolution images. The omission errors falling within 1 or 2 pixels of the edges for high-resolution images can be as much as 50% to 60%. This work improves the understanding of spatial distribution of change-detection errors caused by misregistration and shows the relations between these errors and image edges. Moreover, it is helpful for developing new methods by combining edge and spatial information to reduce the adverse effects of misregistration on change-detection.     

General Adaptive Neighborhood-Based Pretopological Image Filtering

This paper introduces pretopological image filtering in the context of the General Adaptive Neighborhood Image Processing (GANIP) approach. Pretopological filters act on gray level image while satisfying some topological properties. The GANIP approach enables to get an image representation and mathematical structure for adaptive image processing and analysis. Then, the combination of pretopology and GANIP leads to efficient image operators. They enable to process images while preserving region structures without damaging image transitions. More precisely, GAN-based pretopological filters and GAN-based viscous pretopological filters are proposed in this paper. The viscous notion enables to adjust the filtering activity to the image gray levels. These adaptive filters are evaluated through several experiments highlighting their efficiency with respect to the classical operators. They are practically applied in both the biomedical and material application areas for image restoration, image background subtraction and image enhancement.     

相似邻居数目图像脉冲噪声滤波算法

为了去除图像随机脉冲噪声的同时保留边缘,提出一种新方法。该方法首先利用图像局部灰度相似性来构造相似邻居数目图,一个像素的相似邻居数目在窗口内最大或数值较大,才可能认为是没有受到噪声干扰的像素。根据噪声密度不同采用不同方法检测。实验结果表明,阈值能适应性不同图像类型,滤波结果优于大部分已有算法,且算法复杂度低于大部分改进的中值滤波算法。     

An image restoration by fusion

To deal with the problem of restoring images degraded with Gaussian white noise, the mean and adaptive Wiener filters are the most common methods to be implemented. Although these methods are both lowpass in character, they yield different results on the same problem. The mean filter reduces more noise than the adaptive Wiener but also blurs the image edges, whereas the adaptive Wiener filter can preserve edge sharpness but reduces less noise than the mean filter. Instead of trying to design a single mathematical technique to have the advantages of both methods, which is usually theoretically difficult, we propose an alternative solution to this image restoration by fusing multiple image filters using the mean, Sobel, and adaptive Wiener filters. Performance of the fusion algorithm is based on both redundant and complementary information provided by different filters. Several experimental results show the effective application of the proposed approach.     

Noise reduction in synthetic aperture radar images using fuzzy logic and genetic algorithm

Synthetic aperture radar (SAR) is a self-illuminating imaging technique; it produces high resolution images in all weather conditions, day and night. SAR images are widely accepted and used by many application scientists. However, the SAR images are corrupted with speckle noise. Speckle noises are caused by random interference of electromagnetic signals scattered by the object surface within one resolution element. The amount of noise and distribution of noise corrupting the image is unpredictable. Conventional noise filters are quantitative in nature; they are not well suited for uncertainty problems. Fuzzy logic is capable of handling uncertainty. In this work, noisy pixels in the images are identified by using fuzzy rules and filtered using fuzzy weighted mean, keeping the healthy pixels unchanged. The optimum value of parameters used in defining fuzzy membership function is determined by using genetic algorithm (GA). Reducing noise and simultaneously preserving image details are the two most desirable characteristics of noise filters. Peak signal-to-noise ratio (PSNR) and edge preserving factor (EPF) are used to evaluate the performance of the proposed fuzzy filter. SAR images affected by varying amounts of speckle noise are used to evaluate the performance. It was observed that the proposed filter suppresses noise and preserves image edges.

     

基于滤波器的局部自适应全变分图像去噪模型

综合利用冲击滤波器和非线性各向异性扩散滤波器对含噪图像做预处理,然后基于边缘检测函数建立反映图像局部特征的自适应权函数,构建能同时兼顾图像平滑去噪与边缘保留的局部自适应性的全变分模型,并建议用本原对偶算法快速求解。实验结果表明,同传统的全变分图像去噪模型相比,该局部自适应全变分模型在消除噪声的同时能很好地保持图像的边缘轮廓和纹理等细节特征,得到的复原图像在客观评价标准和主观视觉效果方面均有所提高。     

A Strategy for Reduction of Noise in Segmented Images. Its Use in the Study of Angiogenesis

A strategy for reduction of noise in segmented images, which represent the angiogenesis process in tumour tissues, is discussed. With the purpose of comparison we carried out several researches with different morphological filters. However, morphological filters do not reduce completely the noise resulting after the segmentation of blood vessels. Therefore, we propose an alternative scheme based on components labelling and morphological filters. Final results were compared with manual segmentation carried out by an expert, where difference errors of less than 3% were observed. It is demonstrated by extensive experimentation, by using real image data, that the proposed strategy is fast and robust in the environment of a personal computer. These images will be subject to a further morphometrical analysis, in order to diagnose and prognosticate automatically malign tumors.     

Real-time implementation of order-statistics-based directional filters

Vector filters based on order-statistics have proved successful in removing impulsive noise from colour images while preserving edges and fine image details. Among these filters, the ones that involve the cosine distance function (directional filters) have particularly high computational requirements, which limits their use in time-critical applications. In this paper, we introduce two methods to speed up these filters. Experiments on a diverse set of colour images show that the proposed methods provide substantial computational gains without significant loss of accuracy.     

Minimising retinal vessel artefacts in optical coherence tomography images

Optical coherence tomography (OCT) is commonly used to investigate the layers of the retina including retinal nerve fiber layer (RNFL) and retinal pigment epithelium (RPE). OCT images are altered by vessels on the retinal surface producing artefacts. We propose a new approach to compensate for these artefacts and enhance quality of OCT images. A total of 28 (20 normal and 8 glaucoma subjects) OCT images were obtained using Spectralis (Heidelberg, Germany). Shadows were detected along the image and compensated by the A-Scan intensity difference from surrounding non-affected areas. Images were then segmented and the area and thickness of RNFL and RPE were measured and compared. 10 subjects were tested twice to determine the effect of this on reproducibility of measurements. Shadow-suppressed images reflected the profile of the retinal layers more closely when assessed qualitatively, minimising distortion. The segmentation of RNFL and RPE thickness demonstrated a mean change of 2.4% ± 1 and 6% ± 1 from the original images. Much larger changes were observed in areas with vessels. Reproducibility of RNFL thickness was improved, specifically in the higher density vessel location, i.e. inferior and superior. Therefore, OCT images can be enhanced by an image processing procedure. Vessel artefacts may cause errors in assessment of RNFL thickness and are a source of variability, which has clinical implications for diseases such as glaucoma where subtle changes in RNFL need to be monitored accurately over time.     

An automatic filtering convergence method for iterative impulse noise filters based on PSNR checking and filtered pixels detection

Whether input images are corrupted by impulse noise and what the noise density level is are unknown a priori, and thus published iterative impulse noise filters cannot adaptively reduce noise, resulting in a smoothing image or unclear de-noising. For this reason, this paper proposes an automatic filtering convergence method using PSNR checking and filtered pixel detection for iterative impulse noise filters. (1) First, the similarity between the input image and the 1st filtered image is determined by calculating MSE. If MSE is equal to 0, then the input image is unfiltered and becomes the output. (2) Otherwise, one applies PSNR checking and filtered pixel detection to estimate the difference between the tth filtered image and the t–1th filtered image. (3) Finally, an adaptive and reasonable threshold is defined to make the iterative impulse noise filters stop automatically for most image details preservation in finite steps. Experimental results show that iterative impulse noise filters with the proposed automatic filtering convergence method can remove much of the impulse noise and effectively maintain image details. In addition, iterative impulse noise filters operate more efficiently.     

自适应全变分图像去噪模型及其快速求解*

在联合冲击滤波器和非线性各向异性扩散滤波器对含噪图像做预处理的基础上,利用边缘检测算子选取自适应参数,构建能同时兼顾图像平滑去噪与边缘保留的自适应全变分模型,并基于Bregman迭代正则化方法设计了其快速迭代求解算法.实验结果表明,自适应去噪模型及其求解算法在快速去除噪声的同时保留了图像的边缘轮廓和纹理等细节信息,得到的复原图像在客观评价标准和主观视觉效果方面均有所提高.     

图像区域标记和边沿检测的两步法

区域是边沿的互补物，综合考虑二者有利于获得更好的图像分割算法。提出一种基于马尔可夫神经网络的区域标记和边沿检测的两步法。输入图像首先通过一个单层马尔可夫神经网进行多类别的区域标记处理，然后将区域轮廓作为初值，送入另一个神经网络进行边沿检测和修整处理。介绍了一种利用直方图分析的类别初始标记方法。对仿真图像和真实图像的实验均给出了很好的效果。     

基于均值操作的快速自适应滤波器

为了满足图象实时处理对算法速度和高斯噪声,脉冲噪声混合的噪声环境对算法鲁棒性的要求,以及适应能够同时抑制高斯噪声和脉冲噪声的需要,提出了一种可以有效滤除混合噪声（高斯噪声和正负脉冲噪声）,而且可以快速实现的自适应滤波器－－ＡＢＡ滤波器,ＡＢＡ滤波器,ＡＢＡ滤波器应用了自适应的滤波结构,它将以脉冲噪的结果充分利用在自适应处理中,实验仿真所得的数据显示,在脉冲噪声的密度小于１０％的情况下,与其它一些滤     

A new adaptive center weighted median filter for suppressing impulsive noise in images

In this work, a new adaptive center weighted median (ACWM) filter is proposed for improving the performance of median-based filters, preserving image details while effectively suppressing impulsive noise. The proposed filter is an adaptive CWM filter with an adjustable central weight obtained by partitioning the observation vector space. To obtain the optimal weight for each block, the efficient scalar quantization (SQ) method is used to partition the observation vector space. The center weight within each block is obtained by using a learning approach based on the least mean square (LMS) algorithm. The noise filtering procedure is progressively applied through several iterations so that the mean square error of the output can be minimized. Experimental results have demonstrated that the proposed filter outperforms many well-accepted median-based filters in terms of both noise suppression and detail preservation. The proposed new filter also provides excellent robustness at various percentages of impulsive noise.     

基于贝叶斯非局部平均滤波的超声图像斑点噪声抑制算法

超声成像是现代医学影像学最重要的诊断技术之一。然而,由于乘性斑点噪声的存在,使得超声成像的发展受到了一定的限制。针对这种问题,提出了一种贝叶斯非局部平均（NLM）滤波算法的改进策略。首先,运用贝叶斯公式推导出适应于超声图像斑点噪声模型的非局部平均滤波器,由此引出了两种图像块之间距离计算的方式——Pearson距离和根距离;其次,为了减轻计算负担,在非局部区域中选取相似图像块时采用图像块预选择的方式来加速算法;另外,根据多次实验,总结出了一种滤波参数和噪声方差的关系,实现了参数的自适应;最后,利用Visual Studio和OpenCV实现了算法,使得程序的运行时间大幅缩短。为了评估所提算法的去噪性能,在幻影图像和真实超声图像上进行了实验,结果表明：与现有的一些经典算法相比,该算法在去除斑点噪声的表现上有很大提升,并且在保留图像边缘和结构细节方面取得了令人满意的结果。     

一种新的线性混合滤波器

提出一种新的滤波器,称为线性混合滤波器（简称为LMF）,它适用于恢复被一类混合噪音（即一致脉冲噪音与高斯噪音）污染的数字图像。当混合噪音强度在一定范围内变化时,它具有自动调节机制。与若干已知的同类滤波器相比,LMF的速度更快,具有简单而统一的控制参数计算公式,而不是关于参数的选取范围。而且从实验结果可见,用LMF时峰值信噪比得到提高,均方误差得到降低。     

Improving the robustness of variational optical flow through tensor voting

Differential optical flow methods allow the estimation of optical flow fields based on the first-order and even higher-order spatio-temporal derivatives (gradients) of sequences of input images. If the input images are noisy, for instance because of the limited quality of the capturing devices or due to poor illumination conditions, the use of partial derivatives will amplify that noise and thus end up affecting the accuracy of the computed flow fields. The typical approach in order to reduce that noise consists of smoothing the required gradient images with Gaussian filters, for instance by applying structure tensors. However, that filtering is isotropic and tends to blur the discontinuities that may be present in the original images, thus likely leading to an undesired loss of accuracy in the resulting flow fields. This paper proposes the use of tensor voting as an alternative to Gaussian filtering, and shows that the discontinuity preserving capabilities of the former yield more robust and accurate results. In particular, a state-of-the-art variational optical flow method has been adapted in order to utilize a tensor voting filtering approach. The proposed technique has been tested upon different datasets of both synthetic and real image sequences, and compared to both well known and state-of-the-art differential optical flow methods.