Image contrast enhancement using fuzzy clustering with adaptive cluster parameter and sub-histogram equalization

Histogram equalization is an effective technique to boost image quality and contrast enhancement. However, in some cases the increase in image contrast by traditional histogram equalization exceeds the desired amount Which damages the image properties and wanes its natural look. Histogram division and performing a separate equalization for each sub-histogram is one of the presented solutions. The dividing method and determining the number of sub-histograms are the main problems directly affecting the output image quality. In this study, a method is introduced for automatic determination of the number of sub-histograms and density based histogram division leading to appropriate output with no need for parameter setting. Each main peak is in a separate section. Image contrast is increased with no loss of image specifications through determining the number of sub-histograms based on the number of main peaks. The introduced histogram equalization approach consists of three stages. The first stage, using histogram analysis, produces an automated estimate of number of clusters for image brightness levels. The second, clusters the image brightness levels, and using the provided transfer function, the final stage includes contrast enhancement for each individual cluster separately. The results of the proposed approach demonstrate not only clearer details along with a boost in contrast, but also noticeably more natural appearance in the images.     

利用累积分布函数的亮度保持均衡算法

为了解决对比增强算法中的亮度保持问题,提出一个有效的基于直方图的亮度保持和对比增强算法.首先利用累积分布函数计算关键点,这些关键点对亮度保持有重要影响;然后基于关键点将直方图分割为几个子直方图,并对每个子直方图进行均衡;最后将均衡之后的子直方图合并,得到增强的图像.实验结果表明,与已有的一些基于直方图的算法相比,该算法...     

一种区域多直方图红外图像增强方法*

直方图均衡是一种简单有效的红外图像增强技术,但存在着细节信息损失较大的缺陷。为改进这一缺陷,使直方图均衡在增强图像对比度的同时不损失灰度级别,并能增强图像细节特征,提出一种基于区域的multi-HE红外图像增强方法。该方法通过聚类算法将图像分割成多目标区域,据此将直方图分割成多个子图,运用多直方图均衡化对图像进行处理,从而达到在不同目标范围内的图像增强。经过实验验证,该算法能有效地抑制背景区的过增强,扩大了目标区的灰度范围,增强细节部分。     

Adaptive Image Enhancement based on Bi-Histogram Equalization with a clipping limit

A new approach based on Bi-Histogram Equalization is presented to enhance grayscale images. The proposed Adaptive Image Enhancement based on Bi-Histogram Equalization (AIEBHE) technique divides the input histogram into two sub-histograms, which are at the threshold of the histogram median for mean brightness preservation. Histogram clipping is performed to control the enhancement rate, and then the clipped sub-histograms are equalized and integrated to obtain the enhanced image. The novelty of AIEBHE is its flexibility in choosing the clipping limit that automatically selects the smallest value among histogram bins, mean, and median values, resulting in the conservation of a greater amount of information in the image. Automatic selection of the clipping limit addresses the issue of over-emphasizing of high frequency bins during histogram equalization. Simulation results reveal that AIEBHE technique outperforms other histogram-equalization-based enhancement techniques in terms of detail preservation and mean brightness preservation.     

Thresholded and Optimized Histogram Equalization for contrast enhancement of images

A novel technique, Thresholded and Optimized Histogram Equalization (TOHE) is presented in this paper for the purpose of enhancing the contrast as well as to preserve the essential details of any input image. The central idea of this technique is to first segment the input image histogram into two using Otsu’s threshold, based on which a set of weighing constraints are formulated. A decision is made whether to apply those constraints to any one of the sub-histograms or to both, with respect to the input image’s histogram pattern. Then, those two sub-histograms are equalized independently and their union produces a contrast enhanced output image. While formulating the weighing constraints, Particle Swarm Optimization (PSO) is employed to find the optimal constraints in order to optimize the degree of contrast enhancement. This technique is proved to have an edge over the other contemporary methods in terms of Entropy and Contrast Improvement Index.     

频域内分位数的蒙古族家具纹样增强算法

为了使灰度图像的细节更加突出、可视性增强,提出一种基于离散余弦变换与分位数算法结合的图像增强方法。通过离散余弦变换提取出图像的低频分量,图像高频分量保持不变。对低频分量进行分位数的细分,使参与增强过程低频分量的增强级别具有选择性,再分别对这些子直方图进行直方图均衡化,使图像对比度增强。将处理后的低频分量与未处理的高频分量进行逆变换,得到增强后的图像。选取蒙古族家具纹样,与传统的自适应直方图均衡化算法及方向自适应插值算法相比较,提出的方法在蒙古族纹样增强方面具有更好的视觉效果,其评价指标也有显著提高。     

Image Contrast Enhancement by Constrained Local Histogram Equalization

Histogram equalization is a widely used image contrast enhancement method. While global histogram equalization enhances the contrast of the whole image, local histogram equalization can enhance many image details by taking different transformation of the same gray level at different places in the original image. However, the local histogram equalization process often results in unacceptable modification of the original image appearance. In this paper, a constrained local histogram equalization method is proposed to balance the conflicting requirements: enhancement of the image details and the maintenance of the overall image appearance. Our method uses the variational form of histogram equalization so that a constraint condition, which forces the local gray level transformations to change continuously in the spatial domain, can be introduced into the equalization process. Experimental results of different kinds of images show the effect of our method.     

基于Delaunay四面体剖分的网格分割算法

为了构建有意义曲面分片,提出一种基于Delaunay四面体剖分的网格分割算法.首先根据Delaunay四面体剖分得到多边形网格内部的四面体,求出每个面上反映网格内部信息的Delaunay体距离;然后对Delaunay体距离进行平滑处理,再对网格上面的Delaunay体距离进行聚类,用高斯混合模型对Delaunay体距离作柱状图的拟合,利用期望最大化算法来快速求得拟合结果;最后结合图切分技术,同时考虑聚类的结果、分割区域的边界平滑和视觉认知中的最小规则,得到最终的网格分割结果.实验结果表明,采用文中算法可以有效地实现有意义的网格分割.     

基于多尺度带限的自适应直方图均衡和数学形态学的医学 X 射线图像对比度增强算法

医学 X 射线图像是临床上应用最广泛的影像之一。由于需要采用低剂量的 X 射线进行成像,而 X 射线 图像存在一个本质的缺陷,就是低对比度。所以,在临床应用中,往往需要对图像对比度进行增强处理。根据 X 射线图像特性,文章提出了基于多尺度带限的自适应直方图均衡和数学形态学的 X 射线图像对比度增强算法。首 先,采用拉普拉斯高斯金字塔变换把图像分解成高频和低频的不同尺度子波段图像;然后对每塔层高频子图像应 用对比度带限的自适应直方图均衡进行处理,相应的各塔层低通子图像使用数学形态学进行增强处理;最后,各 塔层经过增强处理的高频和低频系数,通过拉普拉斯高斯金字塔的逆变换重构出对比度增强的图像。增强图像再 经全局非线性算子进行对比度的增益调整,获得自然的视觉效果。实验结果表明该算法有效地增强了医学 X 射线 图像的对比度,并通过图像对比度评价标准和对比度改进索引度量算法来分析及对比了算法的性能。     

Image contrast enhancement for preserving mean brightness without losing image features

Histogram equalization is a well-known and effective technique for improving the contrast of images. However, the traditional histogram equalization (HE) method usually results in extreme contrast enhancement, which causes an unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method that is composed of an automatic histogram separation module and an intensity transformation module. First, the proposed histogram separation module is a combination of the proposed prompt multiple thresholding procedure and an optimum peak signal-to-noise ratio (PSNR) calculation to separate the histogram in small-scale detail. As the final step of the proposed process, the use of the intensity transformation module can enhance the image with complete brightness preservation for each generated sub-histogram. Experimental results show that the proposed method not only retains the shape features of the original histogram but also enhances the contrast effectively.     

保持亮度的多峰值直方图均衡算法

现有的直方图均衡算法在增强图像对比度的同时,输出图像的亮度与输入图像无关,并且在均衡区域产生亮度饱和现象,提出了一种新的直方图均衡算法.以亮度保持的双直方图均衡算法(BBHE)为基础,改进其对子图像的分类方式:根据直方图对图像进行多峰值分解,得到一系列不同范围的子图像,然后对每一个子图像在其相应的灰度范围内进行直方图均衡,最后合并这些子图像的均衡结果.实验结果表明,直方图均衡新算法不仅在保持了输出图像亮度的同时,而且非常有效的解决了在原图像均衡区域产生的亮度饱和问题对图像的影响.     

Visual impact enhancement via image histogram smoothing and continuous intensity relocation

Image contrast enhancement is a fundamental pre-processing stage in applications requiring image processing operations. Among revenues of available approaches, histogram equalization is a popular and attractive candidate method to produce resultant images of increased contrast. However, images obtained from canonical histogram equalization frequently suffer from the accompanying artefacts and give rises to uncomfortable viewing particularly in homogeneous regions. In this work, the problem is tackled using the histogram matching concept where the intensity histogram of the input image is matched to its smoothed version for contrast enhancement. Furthermore, homogeneous pixel intensities are randomly perturbed in order to reduce undesirable artefacts. The resultant image intensities are thus distributed over the available range and an increased image contrast is derived. Satisfactory results are obtained from a collection of benchmark images captured under different conditions to verify the effectiveness of the proposed approach.     

基于自适应定向正交投影分解的图象分割方法

将目标和背景分别对应到灰度直方图中的两个高斯分布是进行图象分割的一种常用方法 ,但复杂图象的直方图往往是多峰的 .为了更好地拟合这种复杂图象直方图的多峰特性 ,提出了一种基于自适应定向正交投影分解的图象分割方法 .该方法首先将这种复杂图象的直方图看作是多个高斯分布的叠加 ,并可通过应用自适应定向正交投影分解法来快速准确地确定每个高斯分布的权值、均值和方差 ,进而计算出各相邻高斯分布之间的最优阈值 ,以用于图象分割 .在此基础上 ,又提出了阈值分离度的概念 ,并将其作为选取最终阈值的指标 .应用实例结果表明 ,该方法能够快速有效地实现复杂图象的多阈值分割 .     

基于统计特征的图像直方图均衡化检测方法

直方图均衡化作为图像对比度增强技术之一,在图像恶意篡改过程中经常被作为隐藏被篡改图像强度变化的手段.本文利用图像直方图和其累积分布函数曲线,提取直方图均衡化的痕迹特征,实现直方图均衡化篡改检测.本文提出的方法首先利用图像直方图累积分布函数的变化趋势自适应地选择提取特征的灰度范围,然后在该范围内分别提取累积分布函数与恒等...     

Image contrast enhancement for intelligent surveillance systems using multi-local histogram transformation

Among all applications to monitor the safety and security of working environments, surveillance systems that use computer vision are the most efficient and intuitive in the manufacturing industry. This paper introduces a new technique of contrast enhancement for surveillance systems using computer vision. The histogram equalization method is a common and widespread image enhancement method which maximizes the contrast of the image. This contrast enhancement method usually improves the quality of images, but it can suffer from visual deterioration caused by excessive histogram modification. To overcome the limitations of conventional contrast enhancement methods, this paper introduces a new multi-local histogram transformation method for surveillance systems. This technique is based on the local histograms, which are separated from the overall histogram of the image, and the contrast of the image can be enhanced through two major processes: range reassignment of local histograms and local histogram equalization. The multi-local histogram transformation in this paper enhances the contrast of images, preventing excessive compression and extension of image histograms. The performance of the suggested contrast enhancement method is verified by the experiments in four different environments.     

A real-time image processor with combining dynamic contrast ratio enhancement and inverse gamma correction for PDP

Inverse gamma correction must be performed before displaying the received video signal because alternating current plasma display panel (AC PDP) has a linear output luminance response to a digital-valued input. At the same time contrast ratio enhancement is necessary for improving the image quality of display devices. The histogram equalization (HE) is an important contrast ratio enhancement method. But sometimes HE can produce unrealistic effects in images. In this paper, a new method of combining dynamic contrast ratio enhancement and inverse gamma correction for AC PDP is proposed. The dynamic contrast ratio enhancement and the inverse gamma correction are realized simultaneously in the proposed method. Furthermore the over-enhancement caused by the traditional HE can be avoided. A real-time image processor with the proposed method was designed and implemented. Simulations and experimental results on a 50-in. AC PDP show that the image quality of AC PDP can be improved obviously.     

通道修正均衡化的水下图像增强算法

水中介质和微粒的影响导致光波传播衰减和散射, 在成像过程中水下图像会出现模糊和色偏等情况, 这些 水下成像退化的情况给水下的目标识别、目标跟踪、特征提取等应用带来困难. 针对以上问题, 本文提出了一种基 于通道修正均衡化的暗通道先验(CCD)水下图像增强算法: 首先是对色偏的水下图像进行通道修正均衡化, 利用直 方图强度中心做一个映射, 并将映射的三通道信息融合到限制对比度自适应直方图均衡化中, 改善了图像色偏和对 比度不足的情况; 其次是通过暗通道先验算法进行去模糊, 通过水下增强图像数据集的实验表明, CCD比现有算法 更有效地应对了水下图像成像退化问题, 取得了更好的图像质量指标; 此外, 在特征检测预处理步骤中, 本文方法能 够将检测特征点数提高约1.88倍.     

基于粗糙集和属性直方图的医学图像增强

医学图像信息存在着复杂性,在处理中的各个不同层次可能出现不完整性和不确定性。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,将它用于子图划分。在此基础上本文提出了一种基于粗糙集和属性直方图的医学图像增强方法。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,对噪声点用中值滤波进行滤除,去噪后的背景子图和目标子图进行增强变换,合并得到增强图像。以胸部CT图像中的肺组织为目标区域,进行大量的实验,结果表明该方法明显增强了图像且不损害图像的边缘。     

Multiple layers block overlapped histogram equalization for local content emphasis

Although local histogram equalization (LHE) can be used to emphasize local content in an image, several problems are associated with it. First, LHE tends to amplify speckle noise. Second, this method is not able to preserve the shape of the input histogram, which means the lost of information. Previous literatures also do not give any suggestion on selecting a proper window size for LHE. Therefore, an extension to LHE, which we call as multiple layers block overlapped histogram equalization (MLBOHE), has been introduced. This method consists of three stages, which are enhancement stage, noise reduction stage, and merging stage. Experimental results show that MLBOHE is better than some well-known histogram equalization based local enhancement methods.     

一种广义直方图及其在彩色图像增强中的应用

提出了一种广义直方图的构造方法并将其用于彩色图像均衡化增强。针对传统直方图均衡化方法实现彩色图像增强并不具有普适性的不足,将传统灰度图像直方图定义进行修改并得到一种广义灰度图像直方图,将其用于彩色图像在HSV空间实现均衡化增强。实验结果表明,所建议的广义直方图均衡化彩色图像增强方法是有效的,且比传统直方图均衡化方法能取得更好的增强效果。