基于静态小波分解的多尺度SAR图象滤波

由于雷达回波的相干性 ,合成孔径雷达 (SAR)图象上存在着斑点噪声 ,因此 ,为消除这种噪声 ,提出了一种基于静态小波分解的硬阈值滤波方法 ,该方法首先将 SAR图象分解至静态小波域 ,然后在静态小波域中将噪声的小波系数收缩至零 .将此算法应用于 ERS- 1SAR图象斑点噪声滤波 ,并与基于 Mallat分解的滤波算法和另外 3种典型的 SAR图象滤波算法进行比较 ,结果表明 ,该方法不仅可以有效地去除斑点噪声 ,并且可以保持 SAR图象的精细纹理结构     

基于静态小波分解的多尺度SAR图像滤波

由于雷达回波的相干性，合成孔径雷达（SAR）图象上存在着斑点噪声，因此，为消除这种噪声，提出了一种基于静态小波分解的硬阈值滤波方法，该方法首先将SAR图象分解至静态小波域，然后在静态小波域中将噪声的小波系数收缩至零，将此算法应用于ERS－1 SAR图象斑点噪声滤波，并与基于Mallat分解的滤波算法和另外3种典型的SAR图象滤波算法进行比较，结果表明，该方法不仅可以有效地去除斑点噪声,并且可以保持SAR图象的精细纹理结构。     

Evaluation of various digital image processing techniques for detection of coastal wetlands using ERS-1 SAR data

One of the problems associated with synthetic aperture radar (SAR) data analysis is the presence of random noise or speckle SAR data, being achromatic in nature, which offers very limited scope for the detection and delineation of various terrain features. ERS-1 SAR data for the coastal region of West Bengal, India were processed (a) to suppress the random noise using various filters, (b) to generate the intensity, hue and saturation (IHS) transform from temporal SAR data, and (c) to study the synergism of SAR data with optical sensor data. The results indicate that the Gamma MSP filter with a 5 5 pixel kernel size has been the most efficient in suppressing the noise and concurrently improving the image contrast. The IHS transform of temporal SAR data made it easier to discriminate between various wetland categories. This was also the case with hybrid image generated by the Indian Remote Sensing Satellite (IRS-1B) Linear Imaging and Self-scanning Sensor (LISS-II) data when compared to SAR data alone.     

Structure detection and statistical adaptive speckle filtering in SAR images

Current speckle filters attempt to restore the radar reflectivity using only the multiplicative speckle noise assumption. The best known filters, namely the Frost, Lee or Kuan niters are adaptive filters based on the local statistics, computed in a fixed square window. In this way, the speckle is reduced as a function of the heterogeneity measured by the local coefficient of variation. When the radar reflectivity undergoes significant variations due to the presence of strong scatterers or structural features (edges or lines) in the processing window, such speckle filtering is less effective. In this paper it is shown that the filtering process can be controlled both by the coefficient of variation and by various geometrical ratio detectors. Through shape adaptive windowing, these detectors allow the use of large window sizes for better speckle reduction while preserving spatial resolution and structural features. The backscattered intensity is modelled as

K-distributed within speckled targets and the filter uses a Bayesian approach which allows an explicit use of the multiplicative noise model and the radar reflectivity distribution.     

一种新型的空域SAR图像相干斑抑制方法

为了有效抑制SAR强度图像中的相干斑噪声,提出一种改进Sigma滤波并结合Gamma MAP滤波的空域相干斑抑制方法。首先利用阈值判断法判断并保留强点目标,然后结合SAR图像分布模型和MMSE准则判断Sigma区间,其中可以根据图像局部统计特性自适应调整窗口尺寸,最后选择Sigma区间内像素进行Gamma MAP滤波。实验结果表明:对于星载和机载SAR图像,在相干斑噪声抑制和边缘纹理细节信息保持方面,该方法较其他常用的空域相干斑抑制方法具有明显的优越性,能极大地提高SAR图像判读和目标识别能力。     

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.

     

SAR despeckling neural network with logarithmic convolutional product model

ABSTRACT

Synthetic aperture radar (SAR) images are inevitably contaminated by speckle noise due to its coherent imaging mechanism. Speckle noise obscures the intrinsic radar cross section (RCS) information in SAR images. This article proposes a novel deep neural network architecture specifically designed for despeckling purpose. It uses a convolutional neural network to extract image features and reconstruct a discrete RCS probability density function (PDF). It is trained by a hybrid loss function which measures the distance between the actual SAR image intensity PDF and the estimated one which is derived from convolution between the reconstructed RCS PDF and prior speckle PDF. The network can be trained by either purely simulated image patches or real SAR images. Experiment results on both simulated SAR images and real NASA/JPL AIRSAR images are used to test the performance, and the results show the efficacy of the proposed despeckling neural network compared with three state-of-the-art filters.     

Edge-detection-based filter for SAR speckle noise reduction

Some adaptive filters, such as the Kuan, Lee, minimum mean square error (MMSE) and Frost filters, have been tested on synthetic aperture radar (SAR) data without considering the level of homogeneity in the pixels. Therefore, they degrade the spatial resolution of images and smooth details, while also decreasing the speckle noise level. There are other filters, such as the enhanced Lee and gamma maximum a posteriori (MAP), that utilize the level of homogeneity, but they cannot adequately suppress speckle noise. In addition to these weaknesses, pixels surrounding a point scatterer are also treated as point scatterers due to inadequacy of the method based on evaluating the coefficient of variation for differentiating between them and the point scatterer. We have developed a new method based on the assessment of similarity of homogeneity levels in the image, incorporating edge-detection filters to identify meaningful features and an algorithm to filter the pixels surrounding point scatterers. This method, called the UNSW (University of New South Wales) adaptive filter (UAF), was compared to nine filters using different quantitative and qualitative methods. The results show the ability of the UAF to simultaneously reduce speckle and preserve details as well as its ability to filter more pixels. The effect of increasing the damping factor on speckle noise reduction performance has also been assessed using this method.     

An Anisotropic Fourth-Order Diffusion Filter for Image Noise Removal

Fourth-order nonlinear diffusion filters used for image noise removal are mainly isotropic filters. It means that the spatially varying diffusivity determined by a diffusion function is applied on the image regardless of the orientation of its local features. However, the optimal choice of parameters in the numerical solver of these filters for having a minimal distortion of the image features results in forming speckle noise on the denoised image and a very slow convergence rate especially when the noise level is moderately high. In this paper, a new fourth-order nonlinear diffusion filter is introduced, which has an anisotropic behavior on the image features. In the proposed filter, it is shown that a suitable choice for a set of diffusivity functions to unevenly control the strength of the diffusion on the directions of the level set and gradient leads to a good edge preservation capability compared to the other diffusion or regularization filters. The comparison of the results obtained by the proposed filter with those of the other second and fourth-order filters shows that the proposed method produces a noticeable improvement in the quality of denoised images evaluated subjectively and quantitatively.     

SAR图像MAP降噪的精细研究

本文推导出基于最大后验概率(MAP)滤波的一般形式,给出不同噪声分布和真实图像先验分布条件下的MAP滤波方程.从滤波方程在特定区间上解的分布情况以及区域统计特性分类两方面分析了MAP降噪性能,由此给出了MAP滤波的阈值表达形式.最后给出合成孔径雷达(SAR)图像的MAP降噪试验以及噪声滤除能力的量化指标.为了消除噪声强度对试验结果的影响,全面反映MAP降噪性能,本文给出了降噪能力随噪声大小的动态变化关系.结果表明,真实图像的先验分布对MAP滤波性能有着直接的影响,不合理的先验分布假定会严重降低MAP滤波的降噪能力.     

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.     

Spatially Adaptive Image Restoration Using Fuzzy Punctual Kriging

We present a general formulation based on punctual kriging and fuzzy concepts for image restoration in spatial domain. Gray-level images degraded with Gaussian white noise have been considered. Based on the pixel local neighborhood, fuzzy logic has been employed intelligently to avoid unnecessary estimation of a pixel. The intensity estimation of the selected pixels is then carried out by employing punctual kriging in conjunction with the method of Lagrange multipliers and estimates of local semi-variances. Application of such a hybrid technique performing both selection and intensity estimation of a pixel demonstrates substantial improvement in the image quality as compared to the adaptive Wiener filter and existing fuzzykriging approaches. It has been found that these filters achieve noise reduction without loss of structural detail information, as indicated by their higher structure similarity indices, peak signal to noise ratios and the new variogram based quality measures.     

Iterative deviation filter for fixed-valued impulse noise removal

An iterative deviation filter for fixed-valued impulse noise removal is proposed, with the aim to overcome the defects of existing filters, and further improve the denoising performance. In the proposed filter, a noise detection method based on the extreme intensity values and the deviation of neighbor pixels is proposed, i.e., the pixels with the extreme intensity and differ greatly from the mean of neighbor pixels, are identified as noises. A noise removal method based on the minimum deviation of neighbor pixels is proposed, i.e., the intensity of one neighbor noise free pixel, which is closest to the mean of neighbor noise free pixels, is used as estimated intensity of noisy pixel under consideration. Furthermore, the noise removal strategy performs iteratively and takes full advantage of the previous denoising results. Simulation results show that the proposed method has better denoising performance than the existing distinguished filters in terms of visual representation, peak signal to noise ratio and structural similarity index.

     

A novel adaptive SVR based filter ASBF for image restoration

In this paper, a novel adaptive filter ASBF based on support vector regression (SVR) is proposed to preserve more image details and efficiently suppress impulse noise simultaneously. The main idea of the novel filter ASBF here is to employ a SVR based impulse detector to judge whether an input pixel is contaminated or not by impulse noise. If this case happens, a median filter is employed to remove the corresponding impulse noise. This judgment procedure is executed by regressing the filter window of an input pixel using SVR and then judging the input pixel by its regression distance. Huber loss function is used in SVR regression, due to its excellent robustness capability. The distinctive advantage of the filter ASBF over the latest Support Vector Classifier (SVC) based filter is that no training for the original noise-free image is required in our approach, which is well in accordance with our visual judgment way. Experimental results for benchmark images demonstrate that our filter ASBF here outperforms the extensively-used median-based filters and the SVC based filter.     

改进的基于结构检测的SAR图像去斑方法

合成孔径雷达图像固有的相干斑噪声严重降低了图像的可解译程度,影响了后续目标检测、分类和识别等应用.因此,SAR图像的相干斑抑制问题一直是SAR图像应用的重要课题之一.一个理想的去斑算法应该在平滑的同时保持图像的边缘等细节不受损失,目前存在各种各样的算法,但没有一种方法能够完美的满足这一要求.为此该文提出了一种改进的结构检测的SAR图像去斑算法.利用概率迭代方法分割图像并检测边缘,结合强点检测图,将SAR图像标为结构区和非结构区,在非结构区域内进行Lee滤波以平滑噪声,对结构区直接保留原值,获得了非常好的去斑效果.利用RADARSAT实测图像进行实验,并对实验结果作充分分析,证明了本算法的有效性.     

基于模糊推理的多窗口中央值滤波器设计方法

提出了一种基于模糊推理用于去除图像椒盐噪声的中央值滤波器的新型设计方法,在图像复原处理中,理想的期望是对图像被劣化的部分处理,没有被劣化的部分不作处理,但实际图像处理中处理点是否为噪声点具有模糊性.利用模糊推理对处理点像素多大程度上属于劣质像素进行推定,并且多个模糊滤波器联合使用,处理结果证明对广范围噪声发生率的各种被椒盐噪声劣化的图像复原处理都适用.     

Adaptive two-pass median filter based on support vector machines for image restoration

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.     

基于图像受噪程度的改进模糊加权均值滤波

根据高斯噪声密度大、噪声强度的波动范围宽,其污染图像不仅每一个像素灰度级都会受影响,而且即使是同一灰度级受污染的程度也会不同的特点和传统的图像模糊滤波算法在图像细节保护方面上的不足,提出基于图像受噪程度的改进模糊加权均值滤波算法,该算法根据图像各像素点的受噪程度,得到首次滤波图像和原图像估计直方图,根据该直方图确定模糊隶属度函数,然后对首次滤波图像中灰度小于25的像素点进行模糊加权均值滤波,该算法在不需要期望图像和高斯噪声方差的情况下能有效地去除噪声,同时能够很好地保护图像细节信息。     

基于差分分层噪声检测的开关中值滤波算法

为了精确地检测出图像中的脉冲噪声并滤除,提出了一种差分分层噪声检测的开关中值滤波算法。该算法对噪声检测窗口内像素点按灰度值大小排序,通过差分方法划分出高、低阶噪声块和信号块3部分。当待测像素点属于信号块时视其为信号点;否则,视其为可能噪声点。利用可能噪声点与信号块中与其灰度值最临近的信号点的灰度的差定义了梯度函数,在梯度函数的基础上定义了用于对可能噪声点进行二次检测的模糊隶属函数,对滤波方法进行模糊加权,得到一种加权滤波方法。实验结果证明了该算法对脉冲噪声有很强的抑制作用。     

New spectral estimate for SAR imaging of the ocean

On the basis of the theory of microwave scattering from the ocean surface the correlation function of the synthetic aperture radar (SAR) signal intensity was obtained as a sum of two items. The first item is the proper image correlation function, i.e. the correlation function free of speckle noise SAR signal intensity, which has been investigated in many works (mostly by Alpers et al.). The second item describes the speckle structure in the image. It has been shown that at sufficiently large values of the well‐known velocity bunching parameter the speckle energy is significant within the spectral interval, where the spectrum of large ocean waves is concentrated. In this case the speckle noise can not be suppressed efficiently by means of image filtering. Meanwhile, the mentioned second item is nothing other than half of the correlation function of the complex intensity, which is the square of the SAR signal complex amplitude (unlike the usual real intensity, i.e. the square of the modulus). Therefore, the unspeckled image correlation function can be presented as the difference between the real intensity correlation function and half of the complex intensity function. This leads to a new spectral estimate, free of speckle noise, for the SAR image. The corresponding expression for the new estimate is presented.