CT scan contrast enhancement using singular value decomposition and adaptive gamma correction

We propose in this paper a new enhancement algorithm dedicated to dark computed tomography (CT) scan based on discrete wavelet transform with singular value decomposition (DWT–SVD) followed by adaptive gamma correction (AGC). Discrete wavelet transform (DWT) is considered to decompose the input dark CT image in four sub-bands. Singular value decomposition (SVD) is used in order to compute the corresponding singular value matrix of low–low (LL) sub-band image. The enhanced LL sub-band is determined by scaling the singular value matrix of original LL sub-band by an adequate correction factor, followed by inverse SVD. For a further contrast improvement, the new enhanced LL sub-band image is processed using an AGC algorithm. Finally, the obtained LL sub-band image undergoes inverse DWT together with the unprocessed sub-bands to generate the final enhanced image. This proposed method has the advantage of being fully automatic and could be applied for dark input images with either low or moderate contrast. Different dark CT images are considered to compare the performance of our proposed method to three other enhancement techniques using both objective and subjective assessments. Simulation results show that our proposed algorithm consistently produces good contrast enhancement, with best brightness and edges details conservation and with minimum added distortions to the enhanced CT images.     

Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD

In this paper, an improved and simple approach for enhancement of dark and low contrast satellite image based on knee function and gamma correction using discrete wavelet transform with singular value decomposition (DWT–SVD) has been proposed for quality enhancement of feature. In addition, this method can also process the high resolution dark or very low contrast images, and offers best enhanced result using tuning parameter of Gamma. The technique decomposes the input image into four frequency subbands by using DWT and estimates the singular value matrix of the low–low subband image, and then compute the knee transfer function using gamma correction for further improvement of the LL component. Afterward, processed LL band image undergoes IDWT together with the unprocessed LH, HL, and HH subbands to generate an appropriate enhanced image. Although, various histogram equalization approaches has been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions. The proposed algorithm overcomes this problem using knee function and gamma correction. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than the existing techniques.     

基于小波变换与像元对目标的短波红外图像增强算法

弱光夜视是短波红外成像的重要应用领域之一。针对短波红外弱光图像对比度低,增强后噪声也被放大的特点,提出了一种基于小波变换与像元对目标的短波红外图像增强算法。首先通过小波变换获得不同频率成分的子带图像;然后对低频子带图像进行基于像元对目标的灰度变换处理,对高频子带图像进行可变阈值降噪处理;最后通过小波反变换将处理后的子带重构得到增强结果。将该算法与基于直方图的增强算法,全局优化线性窗口色调映射算法和自然保持增强算法进行比较,采用图像的信息熵和基于Michelson法则的对比度增强度量作为客观评价指标,结果表明本文算法更为有效地提高了短波红外弱光图像的对比度,抑制了噪声的增强,提升了图像的视觉效果。     

基于NSST和人眼感知保真约束的图像自适应增强

鉴于现有的图像增强方法在提高图像对比度、清 晰度等方面仍存在不足,提出了基于非下采样剪切波变换 (NSST,non-subsampled shearlet transform)和人眼感知保真约束的自适应 增强方法。首先对输 入图像进行NSST,分解为一个低频子带图像和多个高频子带图像;然后利用非线性增益 函数增强高 频子带系数,同时对低频部分进行分块局部增强;考虑到传统分块局部增强存在局部图像块 间不连续进而 导致失真的情况,引入了人眼感知保真约束条件,并将其转化为求解一个典型的线性优化问 题,由此获取 增强参数,实现低频部分的增强;最后融合处理后的高低频子带系数,重构出期望的增强 图像。大量实验 结果表明,与近年提出的4种同类方法相比,本文方法所得增强图像的主观视觉效果更好 ,在清晰度、 局部对比度以及全局对比度等定量评价指标上平均高出50%,且实时性良好。     

Low-light color image enhancement based on NSST

In order to improve the visibility and contrast of low-light images and better preserve the edge and details of images, a new low-light color image enhancement algorithm is proposed in this paper. The steps of the proposed algorithm are described as follows. First, the image is converted from the red, green and blue (RGB) color space to the hue , saturation and value (HSV) color space, and the histogram equalization (HE) is performed on the value component. Next, non-subsampled shearlet transform (NSST) is used on the value component to decompose the image into a low frequency sub-band and several high frequency sub-bands. Then, the low frequency sub-band and high frequency sub-bands are enhanced respectively by Gamma correction and improved guided image filtering (IGIF), and the enhancedvalue component is formed by inverse NSST transform. Finally, the image is converted back to the RGB color space to obtain the enhanced image. Experimental results show that the proposed method not only significantly improves the visibility and contrast, but also better preserves the edge and details of images.     

自主空中加油视觉导航系统中的锥套检测算法

在软管式自主空中加油视觉相对导航系统中实现锥套的准确检测至关重要,由于加油机尾流及气流的影响,做随机运动的锥套区域的准确检测是具有一项挑战性的任务。将加油机锥套检测考虑为运动目标检测问题,提出了一种多尺度低秩和稀疏分解锥套检测算法。首先,对锥套图像序列进行平稳小波分解,得到多尺度低频图像序列;再将较粗尺度低频图像序列获得的目标作为下一较细尺度低频图像序列的目标可信图,再进行该尺度的低秩和稀疏分解获得稀疏项,即目标锥套区域。通过在真实加油机锥套图像数据上的实验结果表明,所提算法在进行锥套检测时是有效的。     

A new facet in robust digital watermarking framework

Generally, in watermarking techniques the size of the watermark is very small when compared to the host image. On the contrary, this is an attempt in which a new facet in watermarking framework is presented where the size of host image is very small when compared to the watermark image. The core idea of the proposed technique is to first scale up the size of host image equal to the size of watermark using chaotic map and Hessenberg decomposition followed by the redundant wavelet transform. A meaningful gray scale watermark is embedded in the low frequency sub-band at the finest level using singular value decomposition. To prevent ambiguity and enhance the security, a binary watermark is also embedded in loss-less manner which ensures the authenticity of the watermarked image. Finally, a reliable watermark extraction scheme is developed for extracting both the watermarks. The experimental results demonstrate better visual imperceptibility and resiliency of the proposed scheme against intentional or un-intentional variety of attacks.     

基于FFST和方向特性对比度的图像融合算法

为了克服红外可见光图像融合方法存在的不足,结合快速有限剪切波变换(Fast Finite Shearlet Transform,FFST)的平移不变性以及较高的方向敏感性,提出了一种基于快速有限剪切波变换域的自适应多方向图像融合新方法。首先,对严格配准后的图像进行快速有限剪切波变换分解,得到低频子带和高频子带系数;然后,对低频子带系数采用非负矩阵分解的一个约束稀疏算法,即在基本非负矩阵分解的优化函数中施加稀疏性约束,使分解更优,以此来提高重构后图像的清晰度;高频子带系数则采用联合方向特性的对比度进行选取,以得到丰富的细节信息。最后,利用快速有限剪切波逆变换得到重构后的图像。实验结果表明,融合后的图像充分结合了源图像的有用信息,整体轮廓清晰,在客观评价上也有一定的提高。     

基于方向性对比度金字塔的图像融合方法

基于对比度塔形分解(CP)的图像融合方法具有良好的物理意义,却没有强调方向性的不足,为此提出了一种具有方向性的对比度金字塔图像融合方法.对多聚焦图像进行对比度塔形分解,利用方向滤波器组对高频加方向,得到不同方向的高频子分量.根据不同频率域特点,采用低频分量系数取加权平均、高频分量系数绝对值取大的融合规则,对分解后的子图像进行融合.结果表明:用提出方法得到的融合图像有较高的清晰度和空间分辨率.与基于CP和基于离散小波变换(DWT)的融合方法相比,提出的方法既能保持对比度的含义,又可提供2n个方向信息.     

采用WA-WBA与改进INSCT的图像融合算法

在冗余提升变换与多方向分析相结合的图像融合算法改进的非下采样轮廓波变换(INSCT)中,冗余提升部分采用的Neville算子在多尺度分解的过程中丢失了很多方向信息,不利于后续的多方向分析。针对这一问题,该文提出了一组新算子,可以在频带分解部分很好地保持图像的方向信息,并在此基础上提出一种图像融合算法。该算法先用直方图均衡化的方法提高红外图像中目标区域的灰度值;然后用新算子代替Neville算子对可见光图像与处理后的红外图像进行多尺度分解;最后采用基于权重窗口的活动级与加权求和相结合(WA-WBA)的方法替代简单的加权求和方法对低频子带进行融合,采用一致性测度,实现了各子带系数的自适应融合,有效地弥补了基于像素的图像融合方法的不足。实验结果表明,该算法使融合后的图像更好地保持了可见光的细节信息,同时获得了较清晰的红外图像中的目标信息。     

基于Contourlet 变换和Hu 不变矩的图像检索算法

文章提出一种基于Contourlet 变换和Hu 不变矩的图像检索算法。首先,对每幅图像进行Contourlet 变换,得到低频子带与高频方向子带,把计算得到的低频子带的Hu 不变矩和各个高频方向子带的均值与标准差作为图像的特征向量,利用Manhattan距离进行相似度度量,完成基于内容的图像检索。为对该文提出的算法的检索效果进行检验,分别与基于Contourlet 变换特征的检索算法和基于Hu不变矩特征的检索算法等方法进行了对比实验研究。结果表明,该算法有效地融合了图像的纹理特征与低频子带的形状特征,较好地实现了基于内容的图像检索,平均查准率达到73.94%。     

一种基于平稳小波域的红外图像增强方法

针对红外图像对比度和分辩率低、噪声大的特点,提出一种基于平稳小波域的红外图像增强新方法.对高频子带中幅值较小的噪声系数进行衰减,幅值较大的边缘细节系数进行放大;对低频子带系数的幅值采用所提出的正弦函数进行伸缩处理.实验结果表明,本文提出的方法在有效地增强红外图像对比度及边缘细节的同时,又能很好地抑制背景噪声,综合性能明显优于传统的直方图均衡化和反锐化掩膜增强方法.     

shearlet变换和区域特性相结合的图像融合

为了提高多模医学图像或多聚焦图像的融合性能,结合shearlet变换能够捕捉图像细节信息的性质,提出了一种基于shearlet变换的图像融合算法。首先,用shearlet变换将已精确配准的两幅原始图像分解,得到低频子带系数和不同尺度不同方向的高频子带系数。低频子带系数使用改进的加权融合算法,用平均梯度来计算加权参量,以此来改善融合图像轮廓模糊度高的问题,高频子带系数采用区域方差和区域能量相结合的融合规则,以得到丰富的细节信息。最后,进行shearlet逆变换得到融合图像。结果表明,此算法在主观视觉效果和客观评价指标上优于其它融合算法。     

结合Shearlet变换和果蝇优化算法的甲状腺图像融合

针对甲状腺肿瘤超声图像复杂度高和SPECT图像边界模糊的特点,结合Shearlet变换能够捕捉图像细节信息和果蝇优化算法可靠性高的优势,提出了Shearlet变换和果蝇优化算法相结合的图像融合算法。首先,用Shearlet变换对已精确配准的源图像进行分解,分别得到高低频子带系数。高频子带系数采用区域能量取大的融合规则,低频子带系数使用改进的加权融合规则,并把果蝇优化算法引入低频融合过程,以互信息作为适应度函数来获取最优值,克服了原加权融合算法互信息低的缺点。最后,用Shearlet逆变换得到融合后的图像。实验结果表明,此算法在主观视觉效果和客观评价指标上优于其他融合算法。     

基于Gabor滤波与数学形态学的视网膜图像增强方法

提出一种双尺度多方向的Gabor滤波器与数学形态学结合的视网膜图像增强方法。首先使用双尺度Ga-bor滤波器提取各个方向的主要血管信息并弱化背景,然后使用数学形态学top-hat变换来增强图像微小的细节,最后结合两种方法实现增强的动态调节。实验表明,该方法能够很好地增强视网膜图像中的血管信息,并大范围地弱化了背景和噪声,对于改善视网膜图像的视觉效果有极大帮助。     

基于双域分解的图像增强算法

为解决图像增强中对比度提高与噪声抑制的矛盾,本文提出了一种基于双域分解的图像增强算法,同步实现图像对比度提高与噪声抑制.文中详述了空域分解、分层图像空域增强与变换域降噪、分层图像合成三个主要环节的原理、方法.首先,高斯滤波器将图像分解为基础层和细节层,实现对比度提高与噪声抑制的解耦合;其次,带校正功能的单尺度Retinex和硬阈值收缩的非下采样剪切波降噪算法同步实现基础层的增强和细节层的降噪;最后,分层图像合成、灰度数值延展和微分算子强化,实现合成图像的灰度延展与细节加强,确保增强图像的颜色均匀、细节突出.实验表明,本文算法提高图像对比度和抑制噪声的性能优于其他九种算法.     

基于自嵌入鲁棒双水印的图像认证算法

通过构造鲁棒零水印并鲁棒的嵌入到图像自身来实现完全盲检测的鲁棒水印方案,从而完成数字图像的全盲版权认证。首先利用整数小波变换构造鲁棒零水印:将原始图像进行三级整数小波变换,并将变换后获取的第三级低频子带(LL3)系数二值化来构造鲁棒零水印。然后利用一种基于奇异值分解(SVD,singular value decomposition)的鲁棒水印方案,将构造的鲁棒零水印嵌入到图像小波变换域低频子带(LL1)的分块奇异值分解的U矩阵的系数中。利用超混沌系统设计了随机置乱和加密方案。在鲁棒嵌入之前,先将二值零水印进行置乱分散并加密,增强了方案的鲁棒性和构造水印安的全性。实验结果证明了所提出的方案的有效性。     

基于NSCT和DFRFT的数字水印算法研究

为了有效地克服外界攻击对数字水印的破坏性,增强数字水印的鲁棒性,提出了一种基于非下采样轮廓波变换(Nonsample Contourlet Transform,NSCT)与离散分数阶傅里叶变换(Discrete Fractional Fourier Transform,DFRFT)相结合的数字水印算法.首先,通过Logistic置乱处理水印信号,增强水印的安全性;然后,对宿主图像进行NSCT分解,对其中的低频分量进行DFRFT分解;最后,将置乱的水印图像嵌入到DFRFT分解系数中,从而进一步加强水印的不可见性和鲁棒性.实验结果表明,该算法不仅具有较好的鲁棒性,还可以有效地抵抗外界攻击.     

Image fusion based on shearlet transform and regional features

In this paper, a new image fusion method is proposed based on the shearlet transform. In this method, to make the best use of the regional characteristics of the low frequency coefficients, the low frequency sub-band coefficients from different images are fused according to a rule based on regional energy. As for the high frequency sub-band coefficients, they are fused according to a novel rule upon comprehensive consideration of multiple regional features, such as regional variance, regional average gradient, regional spatial frequency. Experimental results show that the proposed algorithm for image fusion outperforms traditional ones.     

小波变换和改进的FCM算法在医学CT图像分割中的运用

为了更好地运用电脑技术辅助医学诊断,本文研究了一项在小波变换和改正的模糊C均值PCM算法基础上进行医学CT图像分割的方法,以FCM算法为基础,采取小波变换方式针对医学CT图像展开分解,之后运用分解后的低频图的像素点来作为FCM算法的基础点,然后运用马氏距离来进行进一步修正,从而确保更加准确的反应医学图像中的信息.研究结果显示,通过这一方法的处理,医学CT的效果得到了很大的提升.