自适应动态峰值剪切直方图均衡化

传统的直方图均衡化算法在增强图像的同时可能会引入一些视觉退化效应,如一些图像的部分区域出现过度增强。为了克服这个缺点,已有一些灰度均值保持算法,但是这些算法并不能很好地保持图像处理前后灰度均值的稳定性。提出了一种自适应动态峰值剪切直方图均衡化算法：使用滤波器对原图像的直方图进行滤波操作,并且根据图像的信息来确定分割区间及区间数目;对分割的区间进行重新映射;对区间的直方图进行剪切操作,然后分别地进行均衡化处理,并对处理后的图像进行灰度归一化操作。实验结果表明,该算法可以很好地在保持原图像均值的前提下实现图像增强。     

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.     

Bi-histogram equalization using modified histogram bins

The shifting of image mean brightness and the domination of high-frequency bins during histogram equalization (HE) often result in the deteriorating quality of enhanced images and a considerable amount of information loss. This study proposes a novel approach based on bi-histogram equalization to improve its abilities in preserving information entropy and mean brightness. The proposed technique, named Bi-histogram Equalization using Modified Histogram Bins (BHEMHB), segments the input histogram based on the median brightness of an image and alters the histogram bins before HE is applied. Histogram segmentation enables mean brightness preservation, whereas the modification of histogram bins restricts the enhancement rate, thus minimizing the domination effects of high-frequency histogram bins. Simulation results show that BHEMHB significantly outperforms its peers in preserving the details and mean brightness of an image. The output image is visually pleasant with a natural appearance.     

基于对比度受限自适应直方图均衡的乳腺图像增强

采用对比度受限自适应直方图均衡对乳腺图像进行增强,有效地增强了乳腺图像中的细节,如钙化点、乳导管等组织;并通过对算法中相关参数研究,得到应用于乳腺图像增强的参数优选值,以求获得较好的增强效果,为医师分析影像提供方便。通过与灰度直方图均衡的结果进行比较得出：对比度受限自适应直方图均衡为乳腺数字图像增强的有效方法,在计算机辅助乳腺诊断方面有较高应用价值。     

Variance Based Brightness Preserved Dynamic Histogram Equalization for Image Contrast Enhancement

This paper proposes a novel variant of Brightness Preserving Dynamic Histogram Equalization (BPDHE) having more brightness preserving capability with less computational time. This variant, called Variance based Brightness Preserve Dynamic Histogram Equalization (VBBPDHE) uses the interclass and intraclass variance information to segment out the histogram recursively. This variant does not need the smoothing operation of input histogram and also no need to compute local maxima or minima to segment out the histogram unlike BPDHE. Visual analysis, quality metrics and execution time clearly demonstrate the efficiency of the proposed VBBPDHE over well-known existing methods.     

亮度自适应的保熵直方图均衡化方法

直方图均衡化是一种简单有效的图像对比度增强技术,由于它无法保持图像的均值亮度和熵值,因此在实际工程中很少应用。提出了一种直方图规定化的新方法以避免直方图均衡化的缺点,该算法利用直方图均衡化的特点--使直方图分布尽可能均匀,通过变分法求出一个在熵值不变的约束下使得图像均值亮度最大化的直方图,最后将原始直方图转换成直方图规定化后的目标直方图。通过与已有方法HE/DSIHE/MMBEBHE/BPHEME比较,结果表明该方法不仅能够保持熵值,而且可以有效地增强图像对比度,可以用于消费型电子产品中。     

Underwater image enhancement using blending of CLAHE and percentile methodologies

In this paper, a method has been proposed for enhancement of underwater images commonly suffering from low contrast and degraded shading quality. The entirety of the image is changed when we move to capture of images, from air to the water. During capturing some absorption, reflection and scattering effects are induced in the form of contrast, quality and noise as the images look hazy or blurred. This makes one shading to overwhelm the image. For use of underwater resources and overcome these factors the enhancement of the images is required. So, in this paper, we proposed a strategy for underwater image enhancement using Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Percentile methodologies. Finally, these two methodologies are blended for improving the outcomes. Two parameters, namely, Root Mean Squared Error (RMSE) and entropy have been considered for comparing the experimental results of the proposed methodology with the state-of-the-art works. It has been noticed that the proposed system performs better than already existing techniques for underwater image enhancement.     

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.     

基于直方图均衡化与MSRCR的沙尘降质图像增强算法

针对沙尘天气下图像色彩偏移严重及对比度低等问题,提出一种基于直方图均衡化与带色彩恢复的多尺度视网膜（MSRCR）增强的沙尘降质图像增强算法。通过偏色校正和图像增强两个步骤进行图像恢复,将RGB图像各通道预处理后利用限制对比度自适应直方图均衡方法得到校正后的图像,对图像采用双边滤波进行降噪处理,通过MSRCR算法进一步解决色彩失衡问题。由于处理后的图像对比度较低,存在一定色偏,利用伽马校正和基于图像分析的偏色检测及颜色校正方法进行处理得到最终结果。对大量沙尘降质图像进行仿真实验,结果表明,该算法能够有效处理不同偏色程度的沙尘图像,不仅提高了图像的对比度,而且有效避免了图像颜色偏移现象,相比GCANet、MSRCR等算法,平均时间效率提升了46.2%~94.7%。     

自适应直觉模糊相异直方图裁剪的图像增强算法

针对经典的直方图均衡化图像增强算法可能存在的对比度过度增强、亮度分布不均匀和细节信息不突出等问题,提出自适应直觉模糊相异直方图裁剪的图像增强算法.基于直觉模糊集的“投票模型”,引入直觉模糊相异直方图的概念,并基于此提取图像像素的空间位置信息.同时,利用S型隶属度函数对图像直觉模糊相异直方图进行自适应裁剪,采用分段策略对裁剪后的直觉模糊相异直方图进行均衡化处理.最后,利用直觉模糊集的犹豫度刻画原图像的未知信息,修正由引导滤波获得的细节图像,从而保留图像丰富的细节信息.针对3种类型的图像,即自然图像、MRI脑图像及近红外图像的实验结果表明,所提出算法能够有效提高图像的对比度,保留图像的细节信息,使图像呈现较自然的视觉效果,改善图像的质量评价指标.     

浅谈图像增强的直方图处理及其MATLAB实现

图像增强是图像处理的一个重要分支,它对图像整体或局部特征能有效地改善;直方图是图像处理中最重要的基本概念之一,它能有效地用于图像增强.本文主要讨论了直方图均衡化和规定化处理的图像增强技术,并给出了相关的推导公式和算法;同时用MATLAB语言加以实现,给出标准的数字图像在各种处理前与处理后的对照图像、具体算法、实验结果及直方图.结果表明,用直方图均衡化和规定化的算法,能将原始图像密集的灰度分布变得比较稀疏,使处理后的图像视觉效果得以改善,提高其对比度.     

Segment dependent dynamic multi-histogram equalization for image contrast enhancement

Histogram equalization (HE) method proved to be a simple and most effective technique for contrast enhancement of digital images. However it does not preserve the brightness and natural appearance of the images, which is a major drawback. To overcome this limitation, several Bi- and Multi-HE methods have been proposed. Although the Bi-HE methods significantly enhance the contrast and may preserve the brightness, the natural appearance of the images is not preserved as these methods suffer with the problem of intensity saturation. While Multi-HE methods are proposed to further maintain the brightness and natural appearance of images, but at the cost of contrast enhancement. In this paper, two novel Multi-HE methods for contrast enhancement of natural images, while preserving the brightness and natural appearance of the images, have been proposed. The technique involves decomposing the histogram of an input image into multiple segments based on mean or median values as thresholds. The narrow range segments are identified and are allocated full dynamic range before applying HE to each segment independently. Finally the combined equalized histogram is normalized to avoid the saturation of intensities and un-even distribution of bins. Simulation results show that, for the variety of test images (120 images) the proposed method enhances contrast while preserving brightness and natural appearance and outperforms contemporary methods both qualitatively and quantitatively. The statistical consistency of results has also been verified through ANOVA statistical tool.     

Contrast enhancement of images using Partitioned Iterated Function Systems

A new algorithm for the contrast enhancement of images, based on the theory of Partitioned Iterated Function System (PIFS), is presented. A PIFS consists of contractive transformations, such that the original image is the fixed point of the union of these transformations. Each transformation involves the contractive affine spatial transform of a square block, as well as the linear transform of the gray levels of its pixels. The transformation of the gray levels is determined by two parameters which adjust the brightness and the contrast of the transformed block. The PIFS is used in order to create a lowpass version of the original image. The contrast-enhanced image is obtained by adding the difference of the original image with its lowpass version, to the original image itself. The proposed algorithm uses a predefined constant value for the contrast parameter, whereas, the parameters of the affine spatial transform, as well as the parameter adjusting the brightness, are calculated using k-dimensional trees. The lowpass version of the original image is obtained applying the PIFS on the original image repeatedly while using a value for the contrast parameter that is lower than the predefined one. Quantitative and qualitative results stress the superior performance of the proposed contrast enhancement algorithm against four other widely used contrast enhancement methods; namely, linear and nonlinear unsharp masking, Contrast Limited Adaptive Histogram Equalization and Local Range Modification.     

基于Shearlet变换和多尺度Retinex的遥感图像增强算法

传统的小波变换、曲波变换和轮廓波变换无法对图像提供最优的稀疏表示,不能取得好的增强效果,为此,提出了一种基于剪切波(Shearlet)变换的图像增强算法.经Shearlet变换,图像被分解成低频分量和高频分量.首先,对Shearlet变换分解后的低频分量进行多尺度Retinex(MSR)调整,以减轻光照条件对图像的影响;其次,对各尺度、各方向上的高频系数采用阈值抑噪来消除噪声;最后,对重构图像进行模糊对比度增强,提高图像的整体对比度.实验结果表明该算法能够明显改善图像的视觉效果,突出图像的纹理细节且具有良好的抗噪性能.与直方图均衡(HE)、MSR、基于非下采样轮廓波变换(NSCT)的图像模糊增强(NSCT_fuzzy)算法相比,图像清晰度、信息熵、峰值信噪比(PSNR)均有一定的提高,且运行时间缩短为MSR的1/2和NSCT_fuzzy的1/10左右.     

Development of intuitionistic fuzzy special embedded convolutional neural network for mammography enhancement

This article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low‐dose X‐ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low‐dose X‐ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low‐level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and visual quality in mammograms compared with other state‐of‐the‐art methods.     

基于直方图均衡化的手背静脉图像对比度增强

手背静脉图像对比度往往较低,这将影响整个手背静脉识别系统的识别准确率。首先提取手背静脉图像中的有效区域,然后利用直方图均衡化 (HE) 及其各种改进算法对提取的手背静脉图像进行对比度增强处理。实验结果表明,子块部分重叠局部直方图均衡化算法（POSHE）不但能够增强图像的整体对比度,而且图像中细节与背景之间的对比度也得到了增强,同时该算法效率较高,适合于手背静脉图像的对比度增强处理。     

用于数字图像直方图处理的一种二值映射规则

直方图表示数字图像中每一灰度级与其出现频数间的统计关系，它可给出图像的概貌性描述，而基于直方图修改技术的灰度变换是图像增强的实用而有效的处理方法之一。直方图处理包含均衡化和规定化两种技术。均衡化的目的是使图像像素均匀地分布在所有灰度级上；规定化的目的是将原图像的直方图转变为规定的直方图，以便突出一定灰度范围内的图像。为了进一步提高直方图处理算法的有效性，首先分析了现有的几种数字图像直方图均衡化和规定化算法存在的缺点，然后提出了一种新的二值映射规则(BML)，该规则基于最优控制原理，以直方图误差最小为准则进行灰度映射，实验证明．该规则算法简单，无论是用于直方图均衡化处理．还是用于直方图规定化处理，均较其他映射规则都更为有效。     

Adaptive height-modified histogram equalization and chroma correction in YCbCr color space for fast backlight image compensation

Automatic exposure controls in commercially available cameras often encounter difficulties in capturing scenes with backlight luminance which dominates the entire image. An Adaptive Height-Modified Histogram Equalization (AHMHE) algorithm is proposed as a compensation technique for backlight images. It simultaneously enhances contrast in both the dark and the bright areas without creating regions of degraded local contrast. Moreover AHMHE is an adaptive algorithm: thus it requires minimal user input, and its reduced computational requirement makes it suitable for real-time application. In addition to AHMHE, a chroma correction technique was applied to chroma components in the YCbCr color space to produce more vivid color images. A series of subjective and index evaluations were conducted to measure the resultant image quality improvements by the AHMHE and the chroma correction algorithms.     

保持图像亮度的自适应局部对比度增强

目前保持亮度的局部直方图均衡算法用于对比度增强时,大多以亮度均值和中值为图像的亮度分割点,这些方法能较好地保持图像的亮度,但同时也会产生局部过增强。为此提出了一种亮度误差最小的自适应局部对比度增强算法,根据亮度均值绝对误差自适应的选择最佳亮度分割点,然后用保持亮度的双直方图均衡算法对被分割的子图像进行均衡,最后用滤波器消除块效应。实验结果表明,该算法不仅保持了输入图像的亮度,同时也实现了局部对比度增强。     

Detection of clustered microcalcifications in small field digital mammography

The most frequent symptoms of ductal carcinoma recognised by mammography are clusters of microcalcifications. Their detection from mammograms is difficult, especially for glandular breasts. We present a new computer-aided detection system for small field digital mammography in planning of breast biopsy. The system processes the mammograms in several steps. First, we filter the original picture with a filter that is sensitive to microcalcification contrast shape. Then, we enhance the mammogram contrast by using wavelet-based sharpening algorithm. Afterwards, we present to radiologist, for visual analysis, such a contrast-enhanced mammogram with suggested positions of microcalcification clusters. We have evaluated the usefulness of the system with the help of four experienced radiologists, who found that it significantly improves the detection of microcalcifications in small field digital mammography.