一种可控的直方图均衡算法

本文提出一种在传统直方图中利用细节信息的可调的直方图均衡算法。该算法不但能够有效的提高图像的对比度,而且还能增强图像的细节。这种算法可以采用各种不同的边缘信息如:Laplacian、Sobel等。实验证明该方法对增强图像是很有效的。     

一种保持图像细节的直方图均衡新算法 张志龙 李吉成 沈振康

本文分析了传统直方图均衡算法导致图像细节信息丢失和噪声放大的问题,提出了一种新的直方图均衡算法,能够在增强图像整体视觉效果的同时较好地保持图像细节,对图像 噪声也具有一定的抑制作用.实验结果表明,该算法具有较好的性能.     

一种基于改进直方图均衡的图像增强方法

针对传统图像增强方法的不足,提出一种基于模拟频域滤波重构直方图均衡的图像增强方法.将频率域滤波的思想引入空间域直方图运算当中,在模拟频率直方图统计中进行频率信息的统计,利用这些频率信息建立模拟频率域坐标,进行模拟频域滤波,对滤波处理后的直方图进行均衡化处理.实验表明:与传统方法相比,该算法优化了灰度级的动态分布范围,能得到更清晰的增强效果,且在图像中没有视觉明显的噪声放大.     

结合直方图紧凑均衡的图像增强算法

鉴于现有的平台直方图均衡图像增强算法的平台阈值难于选择的问题,文中提出了一种结合直方图紧凑均衡的图像增强算法,将稀疏的直方图分布紧凑化,同时构造自适应平台阈值选取方法,增强图像的细节,限制背景和噪声。该方法克服了传统平台直方图方法整体提升,细节不突出的缺点。目前算法已经嵌入工程硬平台,针对大量低照度图像的实验结果表明,算法能够根据图像内容,自适应选择平台阈值,提升图像细节,较好地抑制背景,同时计算简单,便于实际应用。     

一种自适应多阈值直方图均衡方法

传统的直方图均衡方法不能很好地增强图像的局部细节。针对该问题,在多通道分块直方图均衡方法的基础上,提出一种自适应多阈值直方图均衡方法,根据图像的具体内容自适应地确定通道的数目及分割阈值。实验结果证明,该方法能增强图像的整体和局部细节,有效降噪,耗时较少。     

保持图像细节的直方图均衡算法

对常规的直方图均衡算法进行了改进,避免了不同灰度的像素均衡后被合并、图像细节丢失等现象.实验结果表明,改进的直方图均衡新算法既能充分增强图像的对比度,又能保持图像的局部细节,使图像具有更加自然的视觉效果.     

一种夜间图像增强算法及实现

本文提出了一种夜间图像处理算法。首先采用拉普拉斯算子对锐化图像增强图像细节;然后比较局部灰度均值与全局灰度均值、局部灰度方差与全局灰度方差,确定夜间图像暗色背景区域,并将其分割出来,对分割后的区域使用局部对比度增强的直方图均衡化算法进行灰度调整;最后用Gamma变换柔和图像亮度。Matlab仿真环境实验结果表明：与传统的均衡直方图增强算法相比,该算法在增强夜间图像对比度时,具有更高的信息熵值,更加有效地保护了图像细节。     

保持图像细节的红外图像直方图均衡算法

针对灰度图像经传统直方图均衡算法后因部分灰度级被合并而导致图像细节丢失的问题,提出了一种新的可保持图像细节的红外图像均衡算法.该算法避免了传统直方图均衡带来的灰度断层和丢失现象,对图像灰度级进行无损均衡,提高了图像质量.实验表明该算法使图像的细节和清晰度得到明显的增强。     

基于直方图分割的彩色图像增强算法

针对直方图均衡化直接对彩色图像处理会导致图像色彩失真的情况,在对传统的直方图均衡化方法进行改进的基础上,提出了一种新的带色彩恢复的均衡化算法。该算法对于彩色图像R,G,B各个分量子图的灰度直方图,首先根据其灰度中值和分割直方图等面积原则进行两次分割,同时对分割后的各子灰度直方图分别进行均衡化处理;然后通过计算R,G,B各分量子图的灰度级总数占原彩色图像灰度级总数的比例,将均衡化处理后的R,G,B各分量子图进行合并。实验表明,该算法对彩色图像处理具有较好的增强处理效果。     

基于自适应校正的动态直方图均衡算法

为提高低照度图像的对比度并改进动态直方图均衡化算法的不足,在亮度校正上,提出基于直方图"分布范围"的分割技术和重映射区间分配方法,用去噪阈值优化直方图分布范围的计算;在控制对比度和保留细节信息上,提出带参数的调整量来校正直方图中的较小值,防止灰度级的合并.选取了多个低照度图像数据集进行测试,结合主客观图像质量评价可以看...     

Two-dimensional histogram equalization and contrast enhancement

In this paper, we propose a two-dimensional histogram equalization (2DHE) algorithm which utilizes contextual information around each pixel to enhance the contrast of an input image. The algorithm is based on the observation that the contrast in an image can be improved by increasing the grey-level differences between each pixel and its neighbouring pixels. The image equalization is achieved by assuming that for a given image, the modulus of the grey-level differences between pixels and their neighbouring pixels are equally distributed. The well-known global histogram equalization algorithm is a special case of 2DHE when contextual information is not utilized. 2DHE is easy to implement requiring only a small number of simple arithmetic operations and is thus suitable for real-time contrast enhancement applications. Experimental results show that 2DHE produces better or comparable enhanced images than several state-of-the-art algorithms. The only parameter in 2DHE which requires tuning is the size of the spatial neighbourhood support which provides the contextual information for a given dynamic range of the enhanced image. An automated parameter selection algorithm is also presented. The algorithm can be applied to a wide range of image types.     

A brightness-preserving two-dimensional histogram equalization method based on two-level segmentation

Multimedia Tools and Applications - Histogram equalization (HE) is a classical enhancement method for image processing. However, conventional HE techniques have poor performance in terms of...     

Novel mean-shift based histogram equalization using textured regions

This paper presents a novel mean-shift based histogram equalization method called the MSHE method. The key insight of the proposed MSHE method is that the basis of histogram equalization could be based on textured regions in an image, while impact of smoother regions should be suppressed. Using a mean-shift based approach, the sets of textured regions in an image are determined by finding regions which have a high density of edge concentration. In addition, a new cost function is presented to balance the image quality and contrast enhancement effect for search termination in the proposed algorithm. Based on three typical test images, experimental results show that our proposed MSHE method is quite competitive with the previous eleven methods, such as the HE, BBHE, DSIHE, POHE, RSWHE, DHE, BPDHE, SRHE, GHE, FHE, and THShap.     

基于FPGA的视频直方图均衡化的改进设计

介绍一种改进的直方图均衡算法及其在FPGA上的实现。与传统算法有区别的是如何统计直方图,通常均衡算法是只对亮度分量进行直方图均衡,或者对RGB三色色彩分量分别进行独立的均衡;提出的改进算法对输入像素的RGB分量同时进行统计,合成到一个统一的直方图对RGB通道进行均衡处理。实验结果证明：此算法不仅提高了图像对比度,同时也减轻了偏色或者褪色现象。     

Image contrast enhancement using unsharp masking and histogram equalization

Multimedia Tools and Applications - Contrast enhancement and Mean brightness conservation are two important parameters of image enhancement. A high contrast image is good in subjective quality...     

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.     

Environmental robust speech and speaker recognition through multi-channel histogram equalization

Feature statistics normalization in the cepstral domain is one of the most performing approaches for robust automaticspeech and speaker recognition in noisy acoustic scenarios: feature coefficients are normalized by using suitable linear or nonlinear transformations in order to match the noisy speech statistics to the clean speech one. Histogram equalization (HEQ) belongs to such a category of algorithms and has proved to be effective on purpose and therefore taken here as reference.In this paper the presence of multi-channel acoustic channels is used to enhance the statistics modeling capabilities of the HEQ algorithm, by exploiting the availability of multiple noisy speech occurrences, with the aim of maximizing the effectiveness of the cepstra normalization process. Computer simulations based on the Aurora 2 database in speech and speaker recognition scenarios have shown that a significant recognition improvement with respect to the single-channel counterpart and other multi-channel techniques can be achieved confirming the effectiveness of the idea. The proposed algorithmic configuration has also been combined with the kernel estimation technique in order to further improve the speech recognition performances.