基于色调不变的彩色图像增强

色调是反映彩色图像中色彩信息的重要参数,为了在进行图像效果增强的同时保持色调不变,通常的做法是先把图像从RGB转化到HIS色彩空间,然后在保持H变量(色调)不变的情况下,进行I(亮度)的增强。但是这种方法由于需要进行色彩空间的相互转化,大大增加了计算量。通过对简化HIS模型的分析,提出了一种直接对R、G、B分量进行处理的彩色图像增强方法。该方法可以运用已有灰度图增强算法进行彩色图像的增强,由于不需要进行色彩空间的转化,大大减少了计算量,可用于实时性要求高的场合。实验结果表明,该方案使彩色图像的增强效果明显,运行效率高。     

基于小波变换的彩色图像自适应细节增强算法

针对彩色图像,提出了一种基于小波变换的自适应细节增强算法.首先选择了合适的彩色空间,保持图像的彩色分量不变,对其亮度分量进行小波变换,然后按照分解后的各级近似图像对比度自适应地调整小波细节系数,同时适当地增强近似系数以提高彩色图像的平均亮度,在处理过程中不需要设定额外的调整参数.实验证明,算法不但保留了图像较亮的细节,而且增强了较暗的细节,同时达到了图像色彩不失真的目的.     

PCNN模型在彩色图像增强中的应用

应用灰度图像增强方法对真彩图像进行增强,往往都会产生色彩偏离,影响增强结果和视觉效果。因此基于人眼视觉系统对亮度变化比较敏感,提出在HSV色彩空间,应用PCNN模型进行增强的方法。将真彩图像由RGB空间变换到HSV空间,保持色度H和饱和度S不变,结合入射反射模型,利用脉冲耦合神经网络（PCNN）,对亮度V通道进行增强处理。将HSV空间得到的增强图像变换到RGB空间。实验证实,对一些对比度低、细节不明显的图像应用此方法进行增强,色彩基本无偏差,细节部分明晰,动态范围压缩较好,视觉效果得到了较大的改善。     

基于改进去雾技术的低照度图像增强算法

为提高低照度图像的细节增强效果,本文提出一种基于去雾技术的低照度图像增强算法,首先对低照度图像应用反色操作,然后在反色的图像上执行雾度去除,再次执行反色操作以获得输出图像。随后,在YCbCr色彩空间中,构建一个细节增强网络,用抛物线函数对亮度信息进行增强,拉伸低亮度区域,基本不改变高亮区域的亮度值,保持色彩信息不变,实验证明,本方法可以提高图像的整体细节信息,同时避免了传统方法中出现的为Gamu问题,具有良好的视觉效果。     

一种多尺度彩色图像细节增强算法

针对彩色图像提出一种基于小波变换的细节增强算法,在可分离亮度色度的彩色空间内,保持图像的色度分量不变,对其亮度分量进行小波变换,然后按照分解后的各级近似图像对比度采用插值处理调整小波细节系数,同时适当地增强近似系数以提高彩色图像的平均亮度,在处理过程中不需要设定额外的调整参数.实验证明,本算法不但保留了图像较亮的细节,而且增强了较暗的细节,同时达到了图像色彩不失真的目的.此外,本算法直接在RGB彩色空间内处理彩色图像也获得了较好的效果.     

一种新的低照度彩色图像增强算法*

为了增强彩色图像而不引起色彩失真,在HSV颜色空间中保持色相不变,提出了采用分段对数变换增强饱和度结合在多尺度Retinex算法的基础上,采用边缘保持增强色调的低照度彩色图像增强算法。实验结果表明,该方法在保持图像色相和图像边缘的情况下,显著改善了图像的视觉效果,提高了图像的亮度和对比度。25幅低照度图像的平均亮度、标准偏差和对比度分别提高了94.95%、20.93%和29.88%,相对于带色彩恢复的多尺度Retinex算法的熵和对比度增量分别提高了7.34%和151.51%,效果优于Retinex算法。     

基于小波变换的真彩图像降噪与增强

适用于带噪声彩色图像增强的方法少,而应用灰度图像的增强方法,不仅增强噪声,而且会产生色彩偏离。基于此,提出在HSV色彩空间上的降噪与增强方法。根据人眼视觉系统对亮度的变换敏感性大于色度和饱和度的变换敏感性,以及图像噪声只对亮度和饱和度产生影响,将真彩图像由RGB空间变换到HSV空间,保持色度通道不变,对亮度通道进行小波变换,直接对J尺度下的低频信息进行动态范围的压缩,对各尺度下的高频信息进行基于贝叶斯估计阈值的降噪处理。对饱和度通道,利用得到的降噪公式进行降噪处理,将图像变换到RGB 空间。仿真实验证实,增强后的真彩图像包含噪声少,色彩基本无偏差,图像动态范围压缩良好。     

宏微观信息增强与色彩校正的高效色调映射

目的 色调映射是一种在保持视觉效果基本不变的前提下将高动态范围图像映射到常规低动态显示设备上进行显示的技术。针对现有方法存在细节模糊、边缘光晕及色彩失真等不足,提出一种宏微观信息增强与色彩校正的色调映射新方法。方法 将给定的高动态范围图像映射到HSV(hue, saturation, value)颜色空间,分离亮度信息与色彩信息。基于人类视觉感知机制,在亮度通道构建宏观一致性和微观显著性的亮度感知压缩模型,并进一步通过调节模型缩放因子消除边缘光晕现象。基于颜色恒常性原理,在色度通道构建自适应饱和度偏移模型,融合亮度压缩信息调整图像的饱和度信息,解决色调映射所造成的主观色彩失真问题。结果 实验结果表明,所提算法在结构保真度、自然度和色调映射质量指数等客观评价方面均优于对比色调映射算法,同时主观平均意见值也取得了最高的4.3分（即好—非常好）。结论 宏微观信息增强的亮度感知压缩模型,在确保场景亮度信息不变的情况下,可以有效增强图像纹理细节的完整性和保真性。融合亮度压缩的饱和度偏移模型可以有效解决亮度压缩导致的图像色彩失真等问题。该色调映射算法效率高、通用性强,可广泛应用于图像压缩、生物医学...     

保持图像亮度的直方图均衡新算法

传统的直方图均衡算法能够增强图像的对比度,但是均衡后图像的亮度与输入图像无关,不能保证均衡前后亮度不变.传统直方图均衡对灰度级占较少的进行合并,而这些较少灰度级通常为图像细节部分,因此图像经传统直方图均衡后会导致图像细节部分丢失.本文在RMSHE算法的基础上,提出了一种既能保持图像亮度又能保持图像细节的算法.经仿真验证,图像质量有一定程度的改善.     

一种最大模糊熵多尺度彩色图像增强方法

在HSI颜色空间提出了一种基于视觉特性和最大模糊熵的多尺度彩色图像增强算法。对亮度分量I进行小波多尺度分解,对于不同分解尺度的高频小波子带,采用不同的非线性函数进行处理,以实现图像细节的增强,同时最大限度地抑制噪声。对包含亮度信息的低频小波子带,首先进行基于最大模糊熵的模糊化处理,其中模糊化隶属度函数采用遗传算法进行优化;然后,对经过模糊化的低频小波子带,采用基于人眼视觉特性的线性矫正函数（LRF）进行处理,以实现对图像亮度更好的动态调节。对饱和度分量S,采用指数拉伸方法进行增强处理,色调H保持不变。实验证明,增强后的彩色图像更加清晰,色彩更加鲜艳而不失真。     

一种颜色保持的彩色图像增强新算法

分析了Retinex理论及其典型彩色图像增强算法,在此基础上提出一种颜色保持的彩色图像增强新算法。利用非线性sigmoid传输函数多尺度增强亮度分量,并进行局部对比度增强后获得亮度增益曲面,最后利用亮度增益曲面对原彩色图像RGB三颜色分量同比增强,保证了色调恒定不失真。几种算法实验结果的比较和分析。表明,这种算法是有效的。     

Extraction of spectral reflectance images from multi-spectral images by the HIS transformation model

The process is a completely closed system employing only image data, and it can be applied to any digital multi-spectral data set. A computer technique has been developed to produce spectral reflectance images from multi-spectral images. Hue, intensity and saturation (HIS) colour spatial transformation is used to compute the hue of a three-band colour composite image, and the image pixels with the same hue value are taken as a single material. The average brightness values of the pixels with the same hue are calculated for each band separately, and the distribution of the average values is taken as spectral reflectance image. This spectral reflectance image, which is essentially free of topographic modulation function, but includes spectral information, can be used in image classification, or other image processing. This technique has been successfully applied to recognize ore bearing rock in Inner Mongolia, China by Landsat TM images. The HIS transformation model is a new, very simple and practical technique. It is potentially useful for extracting spectral reflectance information and suppressing the terrain effect.     

基于模糊理论的低照度彩色图像增强算法

低照度彩色图像存在整体亮度低、对比度差、颜色偏暗和信噪比低等特点,传统图像增强算法对其增强效果非常有限。提出了一种基于模糊理论的低照度彩色图像增强算法,将三原色(red,green,blue,RGB)图像转换成色相饱和度(HSV)图像,以保证增强处理不引起图像的色彩失真。对亮度图像进行非线性变化,实现动态范围展宽;采用修正后的隶属度函数将图像映射到模糊平面,实现对比度增强。实验结果表明:该算法显著地提高了图像整体亮度和对比度,改善了低照度彩色图像的视觉效果。     

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.     

Vector Ordering and Morphological Operations for Colour Image Processing: Fundamentals and Applications

: The extension of concepts of greyscale morphology to colour image processing requires the use of a proper ordering of vectors (colours) and the definitions of infimum and supremum operators in an appropriate colour space. In this paper, a new approach to colour image morphology is proposed. It is based on a new ordering of vectors in the HSV colour space that is partial ordering. The proposed approach is hue preserving, and it is not a component-wise technique. Its basic characteristic is that it is compatible to the standard greyscale morphology: its fundamental and secondary operations possess the same basic properties as their greyscale counterparts, and furthermore, it is identical to greyscale morphology when it is applied to greyscale images. Examples that illustrate the application of the defined operations to colour images are provided. Moreover, the usefulness of the new method in various colour image processing applications, such as colour image edge detection, object recognition, vector top-hat filtering and skeleton extraction, is demonstrated. Received: 14 July 2000, Received in revised form: 24 April 2001, Accepted: 19 June 2001     

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。     

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.     

基于亮度评估技术的特征增强衍生图融合算法

针对由动态范围,光照条件,图像捕获设备等因素获得的低亮度图像,提出了一种基于亮度评估技术的特征增强衍生图融合算法来实现亮度较暗图像的对比度调整和特征增强.首先,利用亮度评估技术对低亮度图像的亮度进行评估优化处理,得到曝光率映射;然后,结合曝光率映射和改进的卡方分布函数模型来获取两幅特征增强的衍生图进行融合.最后,利用改进的衍生图融合算法得到最终融合图像.实验结果表明,所提算法的亮度误差,视觉信息保真度,图像互信息等评估参数优于近期方法,在提升图像对比度同时保留了图像良好曝光率区域,并较好地恢复了低亮度区域的边缘以及纹理等细节信息.     

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.