Bi-histogram equalization using two plateau limits

Histogram equalization is an effective method for contrast enhancement on images, but it suffers from some problems such as the tendency to change the mean brightness, loss of information and the introduction of saturation levels which causes an unnatural appearance in the resulting image. Due to the aforementioned problems, a variety of histogram equalization methods have been developed in order to preserve the image brightness, thus avoiding saturation levels that cause loss of information. In this paper, the bi-histogram equalization using two plateau limits (BHE2PL) for histogram equalization is proposed. BHE2PL divides the global histogram into two sub-histograms; then, each sub-histogram is modified by two plateau limits in order to avoid over-enhancement of the image. Experimental results indicate that the BHE2PL method exhibits a better mean brightness preservation compared to methods found in the state of the art; in addition to also presenting a reasonable computation time.     

Shape preserving local histogram modification

A novel approach for shape preserving contrast enhancement is presented in this paper. Contrast enhancement is achieved by means of a local histogram equalization algorithm which preserves the level-sets of the image. This basic property is violated by common local schemes, thereby introducing spurious objects and modifying the image information. The scheme is based on equalizing the histogram in all the connected components of the image, which are defined based both on the grey-values and spatial relations between pixels in the image, and following mathematical morphology, constitute the basic objects in the scene. We give examples for both grey-value and color images.     

基于FPGA的图像自适应AWB系统设计与实现

为了解决常见自动白平衡(Auto White Balance, AWB)方法的场景适应能力不足且实时性较差等问题,提出了一种基于颜色通道直方图重构的自适应AWB方法,并使用现场可编程门阵列(FPGA)对所提出的算法进行硬件电路实现,在校正图像白平衡的同时也确保了系统高速实时处理图像。首先对图像进行限制对比度自适应直方图均衡化(CLAHE)处理来提高图像对比度,然后对图像进行灰度级区间的通道分区统计,对不同场景类别的图像采用颜色直方图匹配或平移的重构方式做自适应处理。实验结果表明,该算法在处理图像白平衡时,相比基于光源估计的AWB算法,色温校正准确率提高了14%,对不同色彩场景有更好的适应性,具有实时处理能力。     

Underlay Device to Device Communication with Imperfect Interference Channel Knowledge

The objective of any night vision system is to enable a person to see in the dark. A low-contrast image puts a contrast constraint on the human observer visibility at night. This is the basic reason for the large number of accidents at night. This research presents two proposed approaches to enhance the visibility of the infrared (IR) night vision images through an efficient histogram processing. The first approach is based on contrast limited adaptive histogram equalization. The second proposed approach depends on histogram matching. The histogram matching uses a reference visual image for converting night vision images into good quality images. The obtained results are evaluated with quality metrics such as entropy, average gradient, contrast improvement factor and sobel edge magnitude.     

An image pixel based variational model for histogram equalization

In this paper, we develop an image pixel based histogram equalization model for image contrast enhancement. The approach is to propose a variational model containing an energy functional to adjust the pixel values of an input image directly so that the resulting histogram can be redistributed to be uniform. This idea is different from existing histogram equalization algorithms where a histogram based on the input image is constructed, a mapping is determined to output a uniform histogram and then the pixel values of the input image are adjusted based on the mapping. In the variational model, a mean brightness term is incorporated to preserve the brightness of the input image, and a geometry constraint can also be added to keep the geometry structure of the input image. Theoretically, the existence of the minimizer of the proposed model, and the convergence of the proposed algorithm are given. Experimental results are reported to demonstrate that the performance of the proposed model are competitive with the other testing histogram equalization methods for several testing images.     

基于自适应局部灰度修正的直方图均衡算法

分析了图像直方图均衡增强算法中信息损失的内在机理,提出基于自适应局部灰度修正的直方图均衡算法。首先对图像作常规直方图均衡处理,然后分析比较处理后图像和原图的梯度变化,以此为依据对图像作局部灰度修正,从而减小直方图均衡处理过程中信息损失的程度,最后通过实验验证了算法的有效性。     

基于改进直方图均衡的红外图像对比度增强算法

针对红外图像灰度分布集中、对比度低的特征,提出了一种基于改进直方图均衡的对比度增强算法。首先采用线性对比度增强将原始16位红外图像映射到8位图像A;然后采用改进的平台直方图均衡将原始16位红外图像映射到8位图像B;再根据输入图像的灰度级范围动态确定映射图像A和B的权值;最后以确定的权值将映射图像A和B合并,得到最终对比度增强的图像。该方法克服了传统平台直方图均衡算法噪声过大及亮度突变的缺点,动态结合了传统的灰度变换增强算法,能根据全图目标与背景灰度的分布情况自适应调整对比度。实验表明,该算法在增强目标对比度的同时有效保留了图像的整体信息,改善了视觉效果。     

基于Piecewise直方图均衡化的图像增强方法

提出了一种基于Piecewise直方图均衡化的图像增强方法,有机地结合了亮度拉伸和传统直方图均衡化的优点。通过定义直方图中相邻灰度级的距离,引入了一个基于直方图均值和标准偏差的Piecewise线性变换,探讨了图像过亮时Piecewise直方图均衡化参数的选取。实验表明,该方法的处理结果能有效避免现有的直方图均衡化方法中的"吞噬"问题和亮度饱和问题。在增强图像时不仅能保留细节,而且具有更加自然的视觉效果。     

基于场景复杂度计算的红外图像平台直方图均衡

直方图均衡类算法由于缺乏限制项,增强后的红外图像易出现灰度级合并和过增强,视觉效果不佳。为解决上述问题,提出基于场景复杂度限制型的平台直方图均衡算法。该算法首先提出对图像场景复杂度进行估计,并将Harris角点数量作为图像复杂度评价依据。然后建立类似sigmoid函数的图像复杂度评价函数,将场景复杂度标准化。最后综合考虑图像复杂度和防止原图均值漂移,依据平台直方图均衡原理获取全局变换函数,重建红外图像。实验结果表明：该方法可有效地依据不同场景实现红外图像增强,不出现过增强和噪声放大。客观评价指标表明,该方法在保持图像均值亮度和图像信息熵方面表现优异。     

一种基于区域分割的直方图均衡算法

红外原始图像对比度低，动态范围高，而且不同场景信号动态范围不一样，为了得到更好的图像效果，需要对红外图像原始数据进行灰度变换。本文针对全局直方图均衡增强算法的不足，提出了一种基于区域分割的直方图均衡增强方法。通过将图像分割成多个区域，每个区域分别做直方图均衡，然后通过线性插值对图像做像素重构，有效地改善了红外热成像系统的图像质量，对比度和图像细节信息得到很大的增强。该算法易于在FPGA中实现，且取得了较好的效果。     

基于聚类分析和骨架提取的含裂纹超声红外热像增强方法研究北大核心CSCD

超声红外热图像因噪声干扰及缺陷位置的热扩散,导致其存在对比度差、清晰度低、边缘模糊等问题。为了增强红外图像视觉效果,提高缺陷检测能力,提出了一种基于聚类分析和缺陷骨架的超声红外图像增强方法。采用基于kmeans的DBSCAN聚类算法对裂纹发热区域进行识别聚类,将图像分解为缺陷生热区域与非缺陷区域;然后,对缺陷区域进行骨架描述,并沿裂纹骨架走向采用改进的部分子块重叠直方图均衡算法对缺陷图像进行增强。提出的超声红外图像增强方法与常用的直方均衡化、限制对比度自适应直方图均衡化、自适应同态滤波三种方法进行对比,结果表明所提的增强方法可以得到对比度更显著的图像,具有明显的优势。提出的方法为增强超声红外图像视觉效果、提升裂纹诊断能力提供了一种有效方法。     

低照度环境监控的图像增强算法研究

在档案库房安防系统中,光照是图像清晰度的重要因素,对后续处理和最终监管有着至关重要的影响.本文针对灰暗天气和夜间等低对比度图像,通过采用直方图均衡化(HE)、自适应直方图均衡化(AHE)和受限对比度自适应直方图均衡化算法(CLAHE)进行图像处理和对比,提出了一种基于受限对比度自适应直方图均衡化的改进算法.该方法首先进行了RGB和彩色空间转换,其次仅对亮度分量进行受限对比度自适应直方图均衡化变换和非线性拉伸变化,最后做RGB的图像输出.实验结果表明,该方法不仅提高了图像的对比度,而且在档案库房安防系统的监控中良好的保持了图像的目标信息,提高了后续识别和监控的有效性.     

一种结合PE的高动态范围红外图像压缩及细节增强算法

针对在提升高动态范围红外图像中潜在或弱小目标细节的同时,还需兼顾噪声抑制、对比度增强的问题,提出了一种基于引导滤波图像分层的动态范围及细节增强算法。对背景层采用平台直方图均衡算法进行压缩,对细节层先采用中值滤波进行去噪,再采用非线性映射对细节中潜在的弱小目标细节进行增强,最后按照一定权重合并得到细节增强后的图像。综合主、客观实验结果,相对于映射类、直方图均衡、双边滤波分层增强等算法,该算法能够在动态范围压缩的过程中提高红外图像目标场景的对比度,突显其纹理特征,取得良好的细节增强效果。     

3-D color histogram equalization by principal component analysis

Color histogram equalization is a method for improving visual appearance of images by enhancing image contrast. Color histogram equalization methods are mostly faced with problems like over-enhancement and brightening. In this paper a new color histogram equalization method is proposed which defines a new three dimensional cumulative distribution function based on a one-dimensional histogram. This one-dimensional histogram is calculated by taking into account the correlation between color channels using PCA. Over-enhancement and brightening are solved by this method because of applying the equalization on a transformed image instead of image itself.     

Contrast enhancement by modified octagon histogram equalization

Histogram equalization is the common method used for contrast enhancement. The mean brightness of the image is adjusted to middle of the permitted range and hence is not suitable for consumer electronics products. A novel contrast enhancement method using modified octagon histogram equalization is developed to overcome the drawback of conventional technique for gray scale images. The proposed algorithm is applied for boat image, microstructure of steel and human head. The contrast enhanced out of the images mentioned is obtained, and the efficiency of the algorithm is evaluated. Simulation results shows that the proposed method can enhance the different types of images effectively. Besides, the proposed contrast enhancement method using modified octagon histogram equalization has comparable performance with black and white stretching and adaptive histogram equalization.     

Adaptive single image dehazing method based on support vector machine

A dehazing method often only shows good results when processing the image for a certain haze concentration. So an adaptive hazy image dehazing method based on SVM is proposed. The innovation points are as follows: Firstly, combining the characteristics of the degraded images of haze weather, the dark channel histogram and texture features of the input images are extracted to form the feature vectors. These are trained by supervised learning through SVM algorithm to realize automatic binary classification of images; Secondly, the defined dehazing methods are called to process the classified result as a hazy image and the same quality evaluation indexes are used to evaluate each image output by different dehazing methods. Then, it outputs the highest evaluation image after haze removal. Finally, the output image is classified again by SVM until the image reaches the clearest it can be. The experimental results show that the proposed algorithm exhibits good contrast, brightness and color saturation from the visual effect. Also the scene adaptability and robustness of the algorithm are improved.     

基于天空约束暗通道先验的图像去雾

针对现有暗通道图像去雾算法存在的天空色彩失真,景物边缘光晕效应等问题,本文提出了基于暗通道理论的改进去雾算法.由于暗原色先验理论不适用于天空区域,本文将引导滤波用于天空区域的细化分割,准确估计包含天空区域图像的大气光照强度,解决了天空色彩失真问题;其次,利用中值滤波得到详细边缘信息,进而得到更为清晰的透射率,有效抑制了景物边缘光晕问题;最后针对去雾后图像偏暗的问题,在HSV空间对亮度分量V通道进行增强处理.实验结果表明,针对带雾图像,本文算法能够有效地去雾,改善天空区域色彩失真以及景物边缘光晕问题.     

一种基于小波变换的图像增强方法

针对部分灰度图像分辨率和对比度低、噪声大、视觉特性差等特点,提出了一种基于小波变换和直方图均衡的新的图像增强方法。该方法将小波变换的多尺度、多分辨率的特点和直方图均衡化的方法相结合,同时再辅以中值滤波去噪和同态滤波增强,从而在很大程度上克服了单一的直方图均衡化进行图像增强时会丢失图像细节信息、增强图像噪声等的不足。实验结果验证文中方法对图像的分辨率和对比度增强有很好的效果。     

基于双直方图均衡算法的红外图像增强

为了抑制全局直方图均衡产生的灰度饱和和局部细节丢失的情况,提出了一种双直方图均衡算法。首先对图像的背景和前景进行分割,提出基于直方图的局部最小值和修正的K-Means聚类算法来确定图像的理想分割阈值,然后再对分割的子图分别作全局直方图均衡（Global Histogram Equalization,GHE）。对该算法进行了实验验证,结果表明,相较于GHE算法,经该算法增强后的图像峰值信噪比（Peak Signal to Noise Ratio,PSNR）提高约16.425%,结构相似度（Structural Similarity Index,SSIM）提高约14.85%。同时通过主观分析,基于直方图局部最小值和修正的K-Means聚类算法的图像分割进行双直方图均衡可以有效抑制GHE算法产生的灰度饱和和细节丢失现象。     

一种自适应红外舰船图像增强算法

复杂环境下海面舰船红外图像信噪比和对比度低、边缘模糊,目前仍没有一种处理效果好且实时性强的图像增强算法。提出一种基于平台直方图均衡化的自适应红外图像增强算法。首先,分析典型海面舰船红外图像及其直方图的特性;在此基础上,提出一种自适应确定目标/背景粗略阈值的方法;最后将灰度大于该阈值的直方图最大值作为平台值,通过平台直方图均衡化算法自适应地完成海面舰船红外图像的图像增强。实验结果表明,该算法不但有效地增强了图像的对比度、抑制了背景,而且具有较快的运算速度。