Segmentation of typhoon cloud image by combining a discrete stationary wavelet transform with a continuous wavelet transform

An algorithm is proposed that employs a multi-threshold technique to segment a typhoon cloud image. For both reducing the noise and enhancing the detail in the typhoon cloud image, the noise is reduced by a Wiener filter and the detail is enhanced by a nonlinear gain operator in the discrete stationary wavelet domain in the proposed algorithm. Then, the histogram equalization technique is used to enhance the global contrast of the processed image. In order to reduce the false peaks of the histogram of the denoised and enhanced typhoon cloud image (DETCI), a Bezier curve is used to smooth the histogram. An optimal segmentation threshold is then obtained from the multiple thresholds obtained from the Bezier histogram. The optimal threshold is used to segment the DETCI. The region of the maximum area in the segmented DETCI is selected as a region of interest (ROI). Thus other objects of small cloud masses are removed by the above method. We replace the area of the ROI with the corresponding area of the original typhoon cloud image to obtain the segmented ROI (SROI). Again the Bezier histogram is used to smooth the false peaks in the histogram of the SROI. In order to detect accurately the peaks and valleys in the curve of the Bezier histogram, a continuous wavelet transform is used to determine the location of peaks and valleys. After the wavelet transform, multi-segmented images at different scales are obtained. A criterion is employed to select an optimal segmentation scale. Finally, the whole typhoon cloud series is segmented accurately by the proposed method. Experimental results show that the proposed algorithm can efficiently segment the typhoon cloud series from a typhoon cloud image, and is better than the Olivo and HQ methods for analysing the structure of the typhoon wind field.     

Adaptive typhoon cloud image enhancement using genetic algorithm and non-linear gain operation in undecimated wavelet domain

By combining discrete undecimated wavelet transform (UWT) with genetic algorithm (GA) an efficient enhancement algorithm for typhoon cloud image is proposed. Having implemented UWT to a typhoon cloud mage, noise in a typhoon cloud image is reduced by modifying the undecimated wavelet coefficients by combining with generalization cross validation at fine resolution levels. GA and non-linear gain operation are used to modify the undecimated wavelet coefficients at coarse resolution levels in order to extrude the details of a typhoon cloud image. Experimental results show that the proposed algorithm can efficiently reduce the additive gauss white noise in a typhoon cloud image while well extruding the detail. In order to accurately assess an enhanced typhoon cloud image’s quality, an overall score index is proposed based on information entropy, contrast measure and peak signal-noise ratio. Finally, comparisons between the proposed algorithm and five other similar methods, are carried out.     

Typhoon cloud image enhancement and reducing speckle with genetic algorithm in stationary wavelet domain

By employing discrete stationary wavelet transform (SWT), generalised cross-validation (GCV), genetic algorithm (GA), and non-linear gain operator, an efficient de-noising and enhancement algorithm for typhoon cloud image is proposed. Having implemented SWT to a typhoon cloud image, noise in a typhoon cloud image is reduced by modifying the stationary wavelet coefficients using GA and GCV at fine resolution levels. Asymptotical optimal de-noising threshold can be obtained, without knowing the variance of noise, by only employing the known input image data. GA and non-linear gain operator are used to modify the stationary wavelet coefficients at coarse resolution levels in order to enhance the details of a typhoon cloud image. Experimental results show that the proposed algorithm can efficiently reduce the speckle in a typhoon cloud image while well enhancing the detail. In order to accurately assess an enhanced typhoon cloud image's quality, an overall score index is proposed based on information entropy, contrast measure and peak signal-noise-ratio (PSNR). Finally, comparisons between the proposed algorithm and other similar methods, which are proposed based on discrete wavelet transform, are carried out.     

Multi-channel satellite cloud image fusion in the tetrolet transform domain

A novel multi-channel satellite cloud image fusion algorithm constructed in the tetrolet transform domain is proposed. Tetrolet is successfully applied in image denoising, image sparse representation, and image restoration. In this paper, tetrolet transform was introduced into the field of satellite cloud image fusion since its sparse degree is high. Tetrolet can describe the geometric structure feature of the satellite cloud image very well. First, tetrolet transform must be implemented into the multi-channel satellite cloud images to obtain low- and high-frequency coefficients and corresponding covering distribution values. Then, a Laplacian pyramid algorithm must be used to decompose the low-frequency portion in the tetrolet domain by averaging the values of its top layer and taking the maximum absolute values of the other layers. While reconstruction is implemented in this stage, the algorithm takes the maximum standard deviation of the high-frequency parts for each block in the tetrolet domain. Last, an inverse tetrolet transform must be used to obtain the final fused image. This paper compares the proposed image fusion algorithm to three similar image fusion algorithms: the curvelet image fusion algorithm, the non-subsampled contourlet transform (NSCT) image fusion algorithm, and the tetrolet image fusion algorithm. Mutual information, joint entropy, mean structural similarity (MSSIM), standard deviation, and average relative deviation are used as objective criteria to evaluate the quality of the fused images. In order to verify the efficiency of the proposed algorithm, the fusion cloud image is used to determine the centre location of eye and non-eye typhoons. Experimental results show that the proposed algorithm performs well when fusing the information in multi-channel satellite cloud images and improves the precision of locating the typhoon’s centre. The proposed algorithm’s comprehensive performance is superior to similar image fusion algorithms.     

Typhoon eye extraction with an automatic SAR image segmentation method

Spaceborne microwave synthetic aperture radar (SAR), with its high spatial resolution (10–100 m), large area coverage, and day/night imaging capability, has been used as an important tool for typhoon monitoring. Since the microwave signal can penetrate through clouds, SAR images reveal typhoon morphology at the sea surface. Within the region of a typhoon eye, wind speed and the associated sea surface roughness are usually low. Therefore, the typhoon eye can be well distinguished as dark areas in SAR images. However, automatic typhoon eye extraction from SAR images is hampered by SAR image speckle noise and other false-alarm dark features contained in an image. In this study, we propose an image processing approach to extract typhoon eyes from SAR images. The three-step image processing includes: (1) applying an extended non-local means image denoizing algorithm to reduce image speckle noise; (2) applying a top-hat transform to denoized imagery to enhance the contrast; and (3) using a labelled watershed to segment the typhoon eye. Experimental results from analysing three Environmental Satellite SAR typhoon images show that our approach provides fast and efficient SAR image segmentation for typhoon eye extraction. Typhoon eyes are segmented correctly, and their edges are well detected. Our experimental results are comparable to manually extracted typhoon eye information. Fine-tuning of this approach will provide an automatic tool for typhoon eye information extraction from SAR images.     

基于第二代Curvelet变换的低对比度图像增强

针对传统图像增强方法用于低对比度图像时,存在对噪声敏感、局部过增强等问题,提出了一种基于第二代 Curvelet 变换的增强方法。将图像进行多尺度多方向的 Curvelet 变换;引入分段非线性函数的思想,调整低频子带系数,提高图像整体的对比度;对各尺度的高频子带系数进行非线性加权,增强图像细节,并进行阈值降噪。实验表明,该方法优于常用的空间域直方图均衡化和小波域图像增强法,能有效地提高图像的对比度、降低噪声,并且较好地保留边缘信息,具有良好的视觉效果。     

基于小波域反正切变换的红外图像增强算法

针对红外图像对比度低、噪声大的特点,利用由信息熵、信噪比和图像标准差构成的评价函数,提出一种平稳小波域的红外图像增强算法。以反正切变换为基础,设计在平稳小波域内增强红外图像对比度的非线性变换函数,并利用差分演化算法与评价函数,寻找最优的非线性变换参数。实验结果表明,与传统的小波域增强算法相比,该算法在有效增强红外图像对比度的同时,对噪声具有较强的鲁棒性。     

Image fuzzy enhancement algorithm based on contourlet transform domain

In order to solve the problems of low contrast, global darkness and noise amplification in some hyperspectral and remote-sensing images, a new image fuzzy enhancement method based on contourlet transform (CT) domain is proposed. The algorithm includes the following four steps. Firstly, the cyclic translation method is used to suppress the pseudo-Gibbs phenomenon caused by the lack of translation invariance of the CT. Secondly, a nonlinear gain function is designed to enhance and suppress the high-frequency coefficients adaptively. Meanwhile, the multi-scale Retinex with mixed gray function is used to process the low-frequency sub-band coefficients. Then, the inverse translation and linear averaging and the inverse CT are performed on the enhanced coefficients, and finally the improved fuzzy contrast is used to enhance the texture and edge of the image globally. The experimental results show that the proposed method can make the image texture details more prominent, and enhance the overall visual effect of the images. Furthermore, the absolute mean difference and PSNR of images are also greatly improved .

     

第二代Curvelet变换在低剂量CT图像增强中的应用

低剂量CT图像信噪比低,图像对比度差。对婴幼儿低剂量颅脑CT图像应用第二代Curvelet变换,分别对低频子带进行非线性增强、对高频子带应用自适应域值方法进行去噪后再进行非线性增强,再经Cuverlet反变换后,其结果与小波变换增强及直方图均衡增强的结果进行比较。结果显示第二代Curvelet变换增强图像对比度及信噪比明显提高,图像细节显示清晰,效果良好。     

台风云图伪彩色增强的Berkeley小波变换法

将Berkeley小波变换(BWT)和线性分配伪彩色增强方法相结合,给出一种有效的台风云图伪彩色增强方法。首先在Berkeley小波域增强云图的细节,然后将增强后的云图分别利用经典的线性分配和改进的线性分配法进行彩色增强。最后将所给出的方法与直接利用经典的线性分配方法、经典离散正交小波预处理后利用改进的线性分配方法进行对比分析。实验结果证明,该方法简便易行,运算量小,在应用中有较大的灵活性,为低对比度台风云图增强处理提供了一种新途径,同时能够有效突出台风眼区和外围螺旋云带等台风结构信息,为后续台风中心定位和台风强度预报提供参考。     

红外图像全局和局部对比度增强的非线性增益法

提出一种基于模拟退火算法和离散平稳小波变换增强红外图像全局和局部对比度的非线性增益算法.基于原始红外图像的灰度直方图提出一种判据,利用该判据判断原始红外图像的对比度类型,以指导模拟退火算法的搜索方向和初值的选取;并利用模拟退火算法优化非线性灰度变换参数,实现对图像进行全局对比度增强.对全局增强后的图像进行离散平稳小波变换,分别对各个分解层的高频子带利用所提出的非线性增强算法进行细节增强.实验结果表明,该算法在有效地提高红外图像整体对比度的同时,能突出红外图像中目标的细节部分信息,在视觉质量上优于传统的直方图均衡法、反锐化掩膜法等.     

基于小波变换的非线性尺度扩散方法

小波变换用于图像处理之所以具有特别的优势，因为它能够聚焦到图像的细微变化。与小波变换不同，用偏微分方程来进行图像处理则需要对图像进行反复的迭代，直到得到一个稳定解，其整个过程是对图像进行整体处理，而且在去噪的同时，可以很好地保持边缘形状不变。若将两者有机结合，则不仅会克服各自的缺点，而且会得到更好的效果。为了在进行图像处理时，既能很好地抑制噪声，又能尽可能多地保持图像细节，提出了一种小波变换与非线性尺度扩散相结合的图像处理方法。该方法是利用小波变换的时频局部性和非线性尺度扩散的边缘增强特性来对图像进行处理，实验结果表明，其不仅能很好地抑制噪声，而且可保留尽可能多的图像细节，可见该方法是有效的。     

无眼台风自动定位方法研究

本文利用GMS红外云图对无眼有螺旋云带的台 风进行了自动定位研究,提出了由云图预处理、云图交互式分割、台风螺旋云带形态特征提 取、螺旋线拟合及定位的四步定位方案,取得了较好的效果．     

雾天条件下的多尺度Retinex图像增强算法

在雾天条件下拍摄图像时,由于受到大气散射作用的影响,图像的颜色和对比度会出现退化现象。为了提高雾天图像的质量,提出一种改进的多尺度Retine雾天图像增强算法。首先采用幂次变换压缩图像动态范围;然后采用非线性变换对图像的高光区域进行抑制;最后采用反锐化掩模滤波消除图像模糊,增强雾天图像细节信息,并采用多幅雾天图像对算法性能进行仿真测试。仿真结果表明,改进多尺度Rctinc的雾天图像增强算法较好地解决了传统Retine算法存在的不足,加快了雾天图像增强的运行速度,使得雾天图像更加清晰化,获得了更优的视觉效果。     

平稳小波域红外图像增强的反正切变换法

针对红外图像噪声大、对比度低等特点,提出一种平稳小波域的红外图像增强方法。综合运用信息熵、信噪比和图像的标准差构造增强后图像的评价函数。结合红外图像的特点,利用反正切变换设计一种简单有效的非线性变换函数用于在平稳小波域内增强红外图像的对比度。该算法用差分演化算法结合构造的评价函数,寻找最优的非线性变换参数。实验结果表明,所提出的算法在有效增强红外图像对比度的同时又对噪声有较强的鲁棒性。所提出的算法综合性能优于两种传统的同类方法。     

Riccati discrete time transfer matrix method for elastic beam undergoing large overall motion

An efficient method for dynamics simulation for elastic beam with large overall spatial motion and nonlinear deformation, namely, the Riccati discrete time transfer matrix method (Riccati-DT-TMM), is proposed in this investigation. With finite segments, continuous deformation field of a beam can be decomposed into many rigid bodies connected by rotational springs. Discrete time transfer matrices of rigid bodies and rotational springs are used to analyze the dynamic characteristic of the beam, and the Riccati transform is used to improve the numerical stability of discrete time transfer matrix method of multibody system dynamics. A predictor-corrector method is used to improve the numerical accuracy of the Riccati-DT-TMM. Using the Riccati-DT-TMM in dynamics analysis, the global dynamics equations of the system are not needed and the computation time required increases linearly with the system’s number of degrees of freedom. Three numerical examples are given to validate the method for the dynamic simulation of a geometric nonlinear beam undergoing large overall motion.     

一种基于视觉特性的彩色图像增强算法

为了增强图像暗区域部分,基于人类视觉系统的全局和局部自适应调节原理,提出一种彩色图像增强方法。该方法主要包括全局自适应亮度调节、局部对比度增强和颜色恢复3个部分。全局亮度调节采用直方图非线性自适应拉伸来增强暗区域的亮度;局部对比度增强利用当前点与区域像素之间的关系,调节当前点的亮度,以增强图像局部对比度;通过一种自适应的非线性颜色恢复算法恢复图像色彩。通过大量图像对比实验分析表明,本文方法可以自适应有效快速地实现图像增强。     

基于视差重映射的立体图像视觉舒适度提升

目的 针对人眼观看立体图像内容可能存在的视觉不舒适性,基于视差对立体图像视觉舒适度的影响,提出了一种结合全局线性和局部非线性视差重映射的立体图像视觉舒适度提升方法。方法 首先,考虑双目融合限制和视觉注意机制,分别结合空间频率和立体显著性因素提取立体图像的全局和局部视差统计特征,并利用支持向量回归构建客观的视觉舒适度预测模型作为控制视差重映射程度的约束;然后,通过构建的预测模型对输入的立体图像的视觉舒适性进行分析,就欠舒适的立体图像设计了一个两阶段的视差重映射策略,分别是视差范围的全局线性重映射和针对提取的潜在欠舒适区域内视差的局部非线性重映射;最后,根据重映射后的视差图绘制得到舒适度提升后的立体图像。结果 在IVY Lab立体图像舒适度测试库上的实验结果表明,相较于相关有代表性的视觉舒适度提升方法对于欠舒适立体图像的处理结果,所提出方法在保持整体场景立体感的同时,能更有效地提升立体图像的视觉舒适度。结论 所提出方法能够根据由不同的立体图像特征构建的视觉舒适度预测模型来自动实施全局线性和局部非线性视差重映射过程,达到既改善立体图像视觉舒适度、又尽量减少视差改变所导致的立体感削弱的目的,从而提升立体图像的整体3维体验。     

基于纹理和区域特征的台风卫星云图分割方法

在利用GMS红外卫星云图进行无眼台风自动定位方法的研究中,台风云系的分割是处理中关键的一步,文章提出了一种基于纹理和区域特征的台风云系分割方法。首先利用图像的分形维数和灰度特征对台风云系中的密蔽云区进行有效的识别后,然后启动基于区域约束的区域生长计算得到台风云系。     

Remote sensing image enhancement based on the combination of adaptive nonlinear gain and the PLIP model in the NSST domain

To enhance image detail and contrast effectively, we present a novel enhancement method for remotely sensed images. This method is based on the combination of adaptive nonlinear gain and the parameterized logarithmic image processing model (PLIP) in the nonsubsampled shearlet transform (NSST) domain. The algorithm works in several stages by deconstructing the image into low- and high-frequency components, applying different functions to each set of frequency components, and then applying further enhancement functions to the reconstructed image. The experimental results show that the proposed method performs well in terms of definition gain, the contrast improvement index (CII) and the measure of enhancement by entropy (EMEE) when compared to several state-of-the-art image enhancement algorithms, including the nonsubsampled contourlet transform (NSCT) with fuzzy field enhancement, the NSCT with unsharp masking, the feature-linking model, linking synaptic computation for image enhancement and improved fuzzy contrast in the NSST domain.