一种改进的直方图均衡实时图像增强算法

直方图均衡是实时图像增强应用较为广泛的一种算法。随着数字图像分辨率的不断提高,现有的算法已经很难满足实时处理的要求。必须找到一种更有效的实时算法来降低图像增强系统对硬件的要求,降低系统成本。因此,介绍现有的几种图像增强算法,并在此基础上提出一种改进的算法。通过对比可以看出该算法有比较好的图像增强效果。     

空间域和频域结合的图像增强技术及实现

图像增强是数字图像的预处理,对图像整体或局部特征能有效地改善.为了实现对数字图像的增强处理,采用时域直方图均衡和频域高频加强滤波相结合的方法对图像进行了增强处理.利用图像中变化剧烈的信息只与高频成分有关这一原理,结合MATLAB设计实现了高频加强滤波器并对图像进行了增强处理,在此基础上使用时域直方图均衡技术再对图像进行处理.试验结果表明,两种技术的结合可以使图像的细部特征更加明显,图像更加锐化,其图像增强效果要好于单独采用其中任意一种技术的处理结果.     

利用粗糙集和属性直方图的图像增强方法

利用粗糙集理论进行图像增强,子图的划分是关键。属性直方图是对直方图概念的推广,是一种由先验知识约束的直方图;将它用于子图的划分,在此基础上提出了一种基于粗糙集理论和属性直方图的图像增强方法。该方法利用属性直方图的 Otsu 算法确定灰度阈值,根据灰度阈值利用不可分辨关系,将图像划分为背景子图、目标子图和噪声子图,对去噪后背景子图和目标子图进行增强变换,并将它们合并得到增强图像。将该方法用于一种海底小目标图像增强。实验结果表明该方法处理增益为 11dB,明显地增强了图像,且不损害图像的边缘。该方法适用于图像有某种先验知识的场合。     

自适应红外图像直方图均衡增强算法

针对红外图像的特点,本文提出了一种自适应红外图像直方图均衡增强算法。该方法根据原始图像的直方图,自适应地构造出一个加权函数对原始图像的直方图进行加权处理,然后采用加权后的直方图对原始图像进行直方图均衡化。算法不需要人为指定阈值,而且克服了传统直方图均衡提升红外图像背景的缺点。实验结果表明,该算法对红外图像具有较好的增强效果,能够有效地抑制图像的背景,突出目标。     

自适应红外图像直方图均衡增强算法

针对红外图像的特点,本文提出了一种自适应红外图像直方图均衡增强算法.该方法根据原始图像的直方图,自适应地构造出一个加权函数对原始图像的直方图进行加权处理,然后采用加权后的直方图对原始图像进行直方图均衡化.算法不需要人为指定阈值,而且克服了传统直方图均衡提升红外图像背景的缺点.实验结果表明,该算法对红外图像具有较好的增强效果,能够有效地抑制图像的背景,突出目标.     

基于属性直方图的迭代自适应图像处理算法

针对滤除图像中噪声与增强有用信息的问题，提出了一种基于自适应权值的误差传递的迭代圈像滤波增强算法．该方法同时利用了图像中像素点间的结构邻接关系和灰度差异信息，使得权值更加合理。图像分割闷值的计算采用属性直方图的Otsu方法．仿真结果验证了该算法的有效性。     

基于图像信息熵统计直方图的图像增强算法

目的为了解决图像因亮度较大造成的成像效果不佳、局部细节不清楚等问题。方法将直方图均衡化技术(Histogram Equalization, HE)引入图像信息熵域,提出对比度弱化的图像信息熵统计直方图自适应均衡化算法(Contrast-reduced Adaptive Entropy Histogram Equalization, CRAEHE)。以各个灰度级信息熵统计值为基础,先将原图像分割成若干个子区域,对每个子区域的灰度信息熵统计值进行阈值截取,补充到子区域内各个灰度级上,再对子区域进行信息熵直方图均衡化处理。采用USC-SIPI和CBSD432数据集图像,用图像灰度均值、标准差、平均梯度、信息熵等参数对实验样本进行质量评价。结果文中算法处理结果较原图灰度均值下降了7.94%,标准差平均提高了52.22%,信息熵平均提高了19.86%,平均梯度提高了57.19%。结论文中算法增强了选自数据集里的过亮图像的细节,并使图像整体细节与质量都得到了改善,该算法的处理结果较其他处理实验样本的主观质量提升明显,对光照强度适应范围广。     

基于双边滤波的Retinex图像增强算法

针对Retinex图像增强技术在高对比度边缘区域存在"光晕伪影"这一缺点,提出了一种基于双边滤波的Retinex算法。该算法先使用带有边缘保存功能的双边滤波将原图像分解成照度图像和反射图像,再分别采用不同的策略压缩照度图像和增强反射图像,最后把两部分图像合成为新的图像。通过从主观视觉效果和客观质量评价两方面对该算法进行检验,实验结果表明它有较好的增强效果,解决了"光晕伪影"问题。     

用频域法确定反卷积的最佳滤波参数

本文介绍了一种确定反卷积的最佳滤波参数的新方法。该方法的特点是，只需在频域就能确定滤波参数，因而节省了大量计算时间。     

时频域滤波方法及其在颤振信号处理中的应用

采用自适应时频分析方法并通过理论分析和数字仿真完成了相应的数值特性与应用特性研究。根据颠振试验的原理和观测信号特点,将所提出的两种时频域滤波算法引入到了飞机结构亚临界响应分析的颠振边界预测研究当中,即通过联合时频分析(JTFA)与时频域滤波提取有效信号再进行模态参数提取与颠振边界预测,取得了预期的效果。     

Image contrast enhancement using histogram equalization with maximum intensity coverage

The histogram equalization process is a simple yet efficient image contrast enhancement technique that generally produces satisfactory results. However, due to its design limitations, output images often experience a loss of fine details or contain unwanted viewing artefacts. One reason for such imperfection is a failure of some techniques to fully utilize the allowable intensity range in conveying the information captured from a scene. The proposed colour image enhancement technique introduced in this work aims at maximizing the information content within an image, whilst minimizing the presence of viewing artefacts and loss of details. This is achieved by weighting the input image and the interim equalized image recursively until the allowed intensity range is maximally covered. The proper weighting factor is optimally determined using the efficient golden section search algorithm. Experiments had been conducted on a large number of images captured under natural indoor and outdoor environment. Results showed that the proposed method is able to recover the largest amount of information as compared to other current approaches. The developed method also provides satisfactory performances in terms of image contrast, and sharpness.     

Contrast enhancement dynamic histogram equalization for medical image processing application

Image processing requires an excellent image contrast‐enhancement technique to extract useful information invisible to the human or machine vision. Because of the histogram flattening, the widely used conventional histogram equalization image‐enhancing technique suffers from severe brightness changes, rendering it undesirable. Hence, we introduce a contrast‐enhancement dynamic histogram‐equalization algorithm method that generates better output image by preserving the input mean brightness without introducing the unfavorable side effects of checkerboard effect, artefacts, and washed‐out appearance. The first procedure of this technique is; normalizing input histogram and followed by smoothing process. Then, the break point detection process is done to divide the histogram into subhistograms before we can remap the gray level allocation. Lastly, the transformation function of each subhistogram is constructed independently. © 2011 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 21, 280‐289, 2011;     

Image deblurring using empirical Wiener filter in the curvelet domain and joint non-local means filter in the spatial domain

In this paper, an efficient image deblurring algorithm is proposed. This algorithm restores the blurred image by incorporating a curvelet-based empirical Wiener filter with a spatial-based joint non-local means filter. Curvelets provide a multidirectional and multiscale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. Our method restores the image in the frequency domain to obtain a noisy result with minimal loss of image components, followed by an empirical Wiener filter in the curvelet domain to attenuate the leaked noise. Although the curvelet-based methods are efficient in edge-preserving image denoising, they are prone to producing edge ringing which relates to the structure of the underlying curvelet. In order to reduce the ringing, we develop an efficient joint non-local means filter by using the curvelet deblurring result. This filter could suppress the leaked noise while preserving image details. We compare our deblurring algorithm with a few competitive deblurring techniques in terms of improvement in signal-to-noise-ratio (ISNR) and visual quality.     

Contrast enhancement using real coded genetic algorithm based modified histogram equalization for gray scale images

Histogram equalization is a well‐known technique used for contrast enhancement. The global HE usually results in excessive contrast enhancement because of lack of control on the level of contrast enhancement. A new technique named modified histogram equalization using real coded genetic algorithm (MHERCGA) is aimed to sweep over this drawback. The primary aim of this paper is to obtain an enhanced method which keeps the original brightness. This method incorporates a provision to have a control over the level of contrast enhancement and applicable for all types of image including low contrast MRI brain images. The basic idea of this technique is to partition the input image histogram into two subhistograms based on a threshold which is obtained using Otsu's optimality principle. Then, bicriteria optimization problem is formulated to satisfy the aforementioned requirements. The subhistograms are modified by selecting optimal contrast enhancement parameters. Finally, the union of the modified subhistograms produce a contrast enhanced and details preserved output image. While developing an optimization problem, real coded genetic algorithm is applied to determine the optimal value of contrast enhancement parameters. This mechanism enhances the contrast of the input image better than the existing contemporary HE methods. The quality of the enhanced brain image indicates that the image obtained after this method can be useful for efficient detection of brain cancer in further process like segmentation, classification, etc. The performance of the proposed method is well supported by the contrast enhancement quantitative metrics such as discrete entropy and natural image quality evaluator. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 24–32, 2015     

基于Retinex和同态滤波的X射线电池图像增强算法

针对工业X射线电池图像对比度低、视觉效果不好、不同区域处理效果差异大等问题,提出一种基于多尺度Retinex和同态滤波的X射线电池图像增强算法.首先利用多尺度Retinex算法中的高斯滤波对照射分量估计,从而得到反射分量.用改进的直方图均衡化方法来处理照射分量,采用改进的巴特沃斯高通滤波器对反射分量的局部细节增强.接着...     

线性修正下的神经网络盲均衡算法

研究了一种利用横向滤波器对神经网络进行线性修正的盲均衡算法。在神经网络的输入层之前加入一个横向滤波器,以横向滤波器的节点输出作为神经网络盲均衡器的输入,利用常数模代价函数分别得到横向滤波器和神经网络盲均衡器的瞬时输出误差,将瞬时误差加权处理作为调节误差分别对横向滤波器和神经网络盲均衡器的权系数进行调节,算法实现了对非凸性误差曲面进行线性和非线性寻优的组合。计算机仿真证明提出的算法有效提高了神经网络盲均衡算法的收敛速度,降低了稳态剩余误差,具有更好的实用性和均衡性能。     

Adaptive contrast enhancement based on histogram modification framework

ABSTRACT

An adaptive contrast enhancement method is proposed in this paper. The proposed method is based on the contrast limited adaptive histogram equalization (CLAHE) and the histogram modification framework. The predefined clip point for the clipped histogram of each block in original CLAHE may still result in excessive contrast enhancement in homogeneous regions, which gives the enhanced image an unnatural look and creates visual artifacts. By replacing the clipped histogram with a modified histogram, the proposed method achieves success in adaptively enhancing contrast in each block based on its content. In addition to this, a novel mapping function is introduced to further improve the enhanced result of histogram equalization (HE). Experiments are conducted with both visible images and infrared images to evaluate the performance of the proposed method. The results show that the proposed method gets better performance on contrast enhancement and visual quality of the enhanced results.     

OFDM水声通信线性最小均方误差算法信道均衡

正交频分复用在高速水声通信中的应用越来越广泛.水声信道是多径、高噪声的衰落信道.由于信道条件差,容易产生码间干扰,信道均衡技术是提高通信系统性能的重要技术.提出了线性最小均方估计在水声通信系统中应用的方案,研究了实际通信中利用调频信号如何确定信号的平均多径时延、最大多径时延,比较了不同调频信号测量信道的性能.该方案基于导频辅助均衡方法,利用调频信号测量信道,获得的信道统计信息,计算信道的自相关矩阵.从而根据线性最小均方误差算法进行信道均衡.通过仿真和湖试进行验证,结果表明该方案效果较好,对系统性能改善比较明显.     

基于逆向滤波器的动态载荷时域识别方法

动态载荷的时域识别方法由于涉及到载荷与响应的复杂卷积关系，是难度较高的复杂的反分析问题。目前针对离散系统的方法都是建立递推连锁计算格式来进行的，递推连锁计算格式的缺点是对初值敏感和误差累积。本文建立了基于结构动力问题的逆向滤波器的计算格式，从根本上解决了初值敏感和误差累积问题，从而提高了数值计算稳定性和动态载荷识别精度。由于使反分析过程转化为类似正分析过程，使文中提出的方法有应用方便、计算效率高的特点，并为动态载荷的实时识别技术建立了理论基础。