基于色调恒定的MSR图像增强算法

由于多尺度Retinex算法增强后图像存在细节信息减弱和颜色失真等不足,本文提出了一种色调恒定的图像增强算法.在原图像中去掉用多尺度高斯函数估计的光照分量,结合参量自适应的非线性函数调整亮度,依据色调恒定的理论保持增强后图像的颜色.与多尺度Retinex比较的实验结果表明,本文算法更有效,增强后的图像不仅细节清晰,而且...     

一种基于单尺度Retinex的雾天降质图像增强新算法

雾图增强是智能管理系统的一个组成部分,在交通管理系统、公路收费站、轮船、飞机场等场合有着广泛的应用场景。比较了对数函数、双曲正切函数、反双曲正切函数对雾天降质图像亮度分量的调节能力,证明双曲正切函数具有比对数函数更宽的亮度调节能力。在此基础上提出一种基于单尺度Retinex的雾天图像增强方法。该算法首先把图像从RGB彩色空间转换到HSV空间,保持色调分量不变,采用中心可自适应调节的双曲正切函数增强图像的全局亮度,局部细节非线性变换处理进一步提高图像的局部对比度,运用线性拉伸对饱和度进行调整,实现颜色补偿。实验结果表明该方法去雾效果显著,且颜色自然。实验还结合方差、熵和算法运算时间等参数,对该算法与多尺度Retinex算法作比较,验证了该算法在图像对比度、细节增强方面的优越性,且算法速度快,具有应用于实时图像处理的能力。     

Retinex彩色图像增强理论的物理思考及其截断区间对图像质量的影响

指出了Retinex彩色图像增强理论的物理意义,在对数空间中,将原图像减去高斯函数与原图像的卷积,其物理本质是除去了原图像中的平滑的部分,突出了原图像中的快速变化的部分,而且高斯函数越尖锐,越是突出图像中的细节,高斯函数越平坦,图像色调保持得越好,多尺度Retinex综合了不同高斯函数与原图像进行卷积的优点。研究了多尺度Retinex标准差截断法,结果是,以多尺度Retinex处理后的图像强度在其平均值附近1倍标准差截断后再拉伸得到的图像,普遍好于以2倍、3倍标准差截断后再拉伸得到的图像。     

基于YCbCr颜色空间的Retinex低照度图像增强方法研究

针对Retinex理论的低照度图像增强算法中光照图像估计问题,提出一种基于YCbCr颜色空间的低照度图像增强方法.该方法将原始低照度图像从RGB(Red Green Blue)颜色空间转换到YCbCr颜色空间,提取该空间中Y分量构建为原始光照图像分量L1(x,y),并对L1(x,y)进行Gamma校正得到增强的光照图像分量L2(x,y),经Retinex模型得到增强图像R(x,y),采用多尺度细节增强方法对图像R(x,y)进行细节增强,得到最终增强图像Re(x,y).实验结果表明,所提方法不仅能有效提升亮度,避免亮度和色彩失真,增强了图像的细节信息并获得了更好的视觉效果,而且运行速度快.     

基于主特征提取的Retinex多谱段图像增强

根据Retinex视觉模型中照射分量和反射分量的统计特性,融合多尺度主特征提取法、平台直方图算法、非局部均值滤波及局部细节增强算法可对多谱段图像进行有效增强.首先利用多尺度主特征提取法估计照射分量,对照射分量进行平台直方图操作,增强全局对比度及图像主结构边缘细节;然后将原图与照射分量相除获取反射分量,对反射分量进行非局部均值滤波抑制噪声,再进行基于局部方差的局部细节增强;最后将增强后的照射分量与反射分量相乘,即为增强图像.从主观和客观两方面,对X光图像、紫外图像、可见光图像、低照度可见光图像和红外图像实验结果的分析表明,本文算法能够有效地抑制图像噪声、增强图像对比度及细节、改善图像视觉效果,是一种通用有效的多谱段图像增强算法.     

基于Retinex理论与概率非局部均值的红外图像增强方法

针对传统红外图像增强算法中细节模糊及过度增强的问题,提出了一种基于Retinex理论与概率非局部均值相结合的红外图像增强方法.首先通过单尺度Retinex方法调整图像中过暗与过亮部分的灰度级;然后利用概率非局部均值对图像进行分解处理得到基本层与细节层,对基本层采用直方图均衡化拉伸对比度,对细节层采用非线性函数进行增强;最后,将不同层次的结果融合得到对比度与细节增强的红外图像.用该方法对多组不同场景的红外图像进行仿真实验,并将其与多种增强方法进行主、客观对比分析,结果表明所提方法在红外图像的细节及对比度增强方面都获得了更好的效果.     

多尺度Retinex截断区间对图像质量影响的分析

研究了多尺度Retinex算法对曝光量不足的彩色图像增强处理的结果。改进型多尺度Retinex算法是使用多尺度Retinex增强后在亮度平均值附近以k倍标准差进行截取、拉伸。采用亮度与对比度乘积、图像信息熵等2个判据,实验结果表明在亮度平均值附近k=1倍标准差进行截取后再拉伸得到的图像其2个判据的值都较大,图像最佳。     

融合暗通道先验和MSRCR的分块调节图像增强算法

针对雾霾天气图像中雾霾浓度分布不均以及色彩失衡等问题,提出一种将暗通道先验算法与带色彩恢复因子的多尺度Retinex算法相结合的分块调节图像增强算法.对大气散射模型以Retinex图像模型中的尺度进行线性映射,得到一个同时具有大气散射模型中的透射率和大气光照值以及Retinex图像模型中入射图像的多参数新模型.根据模型获取去除大气散射图的新原图,并利用不同尺度的引导滤波计算获得整幅图像的入射图像,再结合大气散射光图以及色彩恢复因子得到最终的高频细节图.针对图像中雾霾浓度分布不均的情况将整图划分为多个区域小块,用融合后的算法计算每个区域小块的动态截断值,根据不同的动态截断值可以对整幅图像的高频细节进行动态调整,从而得到多幅局部最优图像,将得到的图像进行像素级等权融合,最后可得到保证各局部细节的最优图像.将本文算法与现有算法在主观视觉和客观评价两方面进行了实验对比,结果表明,该方法可以有效解决图像雾气不均匀以及色彩失衡等问题,明显提高了去雾后图片的质量.     

基于照射_反射模型和有界运算的多谱段图像增强

根据多尺度照射_反射模型, 结合广义有界运算模型和引导滤波, 能够有效地解决多谱段降质图像的增强问题. 算法采用自适应的引导滤波核函数作为环绕函数, 估计反映图像整体结构的不同尺度的低频照射分量; 利用有界广义对数比(general log-radio, GLR)模型加法代替Retinex理论中的对数变换运算; 再由GLR模型减法去除照射分量, 将不同尺度的反射分量从原始图像中分割出来; 对不同尺度反射分量的有效信息采用有界GLR模型乘法和加法进行融合, 有效地避免光晕伪影现象及越界现象的发生, 得到多尺度反射分量图像, 即最终的增强图像. 通过对可见光波段的低照度图像和雾霾图像、红外图像、X光医学图像四组多谱段降质图像实验分析, 以对比度和信息熵作为评价指标, 与同类算法进行了图像增强效果的定性和定量对比, 结果表明本文算法增强后的图像纹理和边缘细节更加丰富、对比度更高、视觉效果更佳, 可广泛地应用于多种图像增强领域.     

基于亮通道和暗通道结合的图像去雾

低照度环境下图片质量会下降。同时,悬浮在空气中的烟雾、粉尘等物质形成的雾、霾,会导致图像的细节模糊不清,对户外拍照和计算机视觉应用造成了极大的影响。因此,对退化图像进行去雾处理,提高图像质量,在图像处理和计算机视觉领域具有非常重要的应用价值。提出一种基于亮通道和暗通道结合的雾霾天气图像去雾算法。基于退化图像的物理模型,提出一种空气光散射模型,通过亮通道和暗通道的结合来估计大气光值和透射率。该算法可以解决有雾图像恢复时天空区域的颜色失真问题,恢复图像的细节和颜色,提高图像的视觉效果。仿真结果表明,本文算法优于多尺度Retinex图像去雾算法。     

Color constancy enhancement under poor illumination

In this Letter, the color constancy and its realization were studied and a novel color constancy image enhancement algorithm under poor illumination was presented. The purpose of this algorithm is to maintain the hue of an image during the processing so that the change of saturation can be minimized. The original image was first multiplied by a scale parameter obtained by the adaptive quadratic function to enhance the luminance, and then the edge details were restored by a shifting parameter. Numerical results of the Simon Fraser University (SFU) image database indicated that the proposed algorithm performed much better in preserving the hue and saturation and avoiding color distortion compared with the existing image enhancement algorithms.     

Retinex-Based Fast Algorithm for Low-Light Image Enhancement

We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.     

Lightness and chroma enhancement for food images considering Helmholtz–Kohlrausch effect

The color appearance of a food image is very important for making it look delicious. Many food images acquired in low illumination environments do not give delicious impressions because of the deterioration of their color appearance. We propose an image enhancement method to improve the color appearance of a food image acquired in a low illumination environment. In the proposed method, the lightness of each pixel is first enhanced within a color gamut on an equal hue plane in the CIELAB color space. The boundary of the color gamut is given by a lookup table calculated in advance. Then, when the hue of the pixel is attributed to foods, the chroma is enhanced within the color gamut. In this case, we consider Helmholtz–Kohlrausch (H-K) effect, which indicates the relation of the hue, chroma, and lightness in human visual perception. According to H-K effect, the perceived lightness increases as the chroma is enhanced. Therefore, the perceived lightness is preserved after the chroma enhancement, so that the food image is not too enhanced. To verify the effectiveness of the proposed method, experiments are conducted using several food images.     

Global brightness and local contrast adaptive enhancement for low illumination color image

An adaptive enhancement algorithm is proposed in the paper for low illumination color image which is low brightness and low contrast. The algorithm is realized through three steps: the global brightness adaptive adjustment, locally adaptive contrast enhancement and color restore. Experiments show that the brightness and local contrast of low illumination color image can be effectively enhanced. The details, especially in the dark region of enhanced image are more prominent and the enhanced image is more vivid than some other methods for low illumination color image enhancement.     

基于主动光照的深海图像增强算法

为了改善主动光在水下传播过程中由散射与吸收效应导致的深海图像对比度低下以及颜色失真问题,提出一种水下图像增强算法.不同于传统方法利用最亮点的强度值作为背景光,提出基于物体与背景光非相关性的背景光估计方法,有效避免了前景处的亮像素或白色物体像素对背景光的误判,同时确保了去散射的精确性,提高水下图像的对比度;针对人造光源的颜色增益和光路衰减导致的图像色偏等问题,在去散射图像上选取离光源最近的灰色像素,利用其对光源的敏感性,将光照强度分离出来.最终,通过估计并去除光源本身的颜色增益,同时补偿光在传播过程中的损失,实现图像的颜色校正.实验结果表明,所提算法可以有效去除水下图像的散射效应,较好地恢复图像色彩,进而获得较优的增强图像.相比于其他算法,增强后的图像信息熵和水下图像质量评价指标值较高,说明该算法能显著提升水下图像的质量,同时保留图像有用信息.     

图像增强算法综述

图像增强算法能够提高图像整体和局部的对比度,突出图像的细节信息,使增强后的图像更符合人眼的视觉特性且易于机器识别,在军事和民用领域具有广泛的应用。本文从图像增强算法的原理出发,归纳总结了近年来应用比较广泛的4类图像增强算法及其改进算法,包括直方图均衡图像增强算法、小波变换图像增强算法、偏微分方程图像增强算法和基于Retinex理论的图像增强算法。结合人眼视觉特性、噪声抑制、亮度保持和信息熵最大化等图像增强的改进算法,在保证增强图像具有较高对比度的前提下,可进一步提升图像的质量。实现了9种较为典型的图像增强算法,采用主观和客观的评价方法对增强效果进行了对比,分析了不同增强算法的优缺点,并给出了这些算法的计算时间。对这些算法的深入研究能够推动图像增强技术向更高水平发展,从而使图像增强技术在多个学科领域发挥重要作用。     

基于神经网络的低照度彩色图像增强算法

由于低照度彩色图像存在整体亮度低、对比度低、颜色偏暗和信噪比低等特点,所以经典图像增强算法对其增强效果非常有限。提出了一种利用BP神经网络进行彩色图像增强的算法,并将RGB图像转换成HSI图像,以保证增强处理不引起图像的色彩失真。实验证明:该方法显著地改善了低照度彩色图像的视觉效果,提高了图像整体亮度和图像的信噪比,可调节图像的动态范围,能增强图像的对比度和细节,可增加图像信息熵。     

Catenary image enhancement using wavelet-based contourlet transform with cycle translation

The image quality is extremely important in the catenary fault diagnosis based on the image processing. In order to improve image visual effects, this paper proposes a catenary image enhancement method based on the wavelet-based contourlet transform (WBCT) with cycle translation. First, the Laplacian pyramid (LP) transform is replaced by the wavelet transform (WT) for reducing the redundancy of the contourlet transform (CT). Meanwhile, combine the WBCT with cycle translation to overcome the problem of WBCT without the translational invariance. Finally, the adaptive enhancement function is chosen to enhance the catenary images. Experimental results show that the proposed method has the advantages of preserving image edge details and texture. In terms of visual effects, signal-to-noise ratio (SNR) and edge preserving index (EPI), the proposed algorithm outperforms the traditional methods.     

Image enhancement through contrast enlargement using the image regions extracted by multiscale top-hat by reconstruction

An image enhancement algorithm based on multiscale top-hat by reconstruction is proposed in this paper. Firstly, multiscale top-hat by reconstruction using multiscale structuring elements is discussed. Then, multiscale bright and black image regions are extracted. Thirdly, useful image regions for image enhancement are obtained from the extracted multiscale bright and black image regions. Finally, after a base image is calculated from the results of the opening and closing by reconstruction operations, the original image is enhanced through combing the useful image regions into the base image. Experimental results on different types of images show that the proposed algorithm is efficient.     

基于光照-反射成像模型和形态学操作的多谱段图像增强算法

为解决多谱段降质图像增强问题,提出了一种基于光照-反射成像模型和形态学操作的多谱段图像增强算法.首先对图像饱和度使用自适应非线性拉伸函数进行拉伸,使增强后的图像色彩更加饱和、自然;接下来利用引导滤波算法提取出图像的光照分量,提出了一种基于细节特征的加权融合策略,利用光照分布特性构造了一种自适应Gamma校正函数对光照分量进行处理,并将其与利用对比度受限的自适应直方图均衡化方法处理后的光照分量以及原始光照分量进行融合;然后在反射分量校正时,构造了一种形态学操作函数来校正反射信息;最后合并光照分量和反射分量,并与处理后的饱和度分量和色调分量一起得到增强图像.采用主客观评价指标对可见光低照度图像、水下图像、高动态范围图像、沙尘暴图像、雾天图像和热红外图像6种降质多谱段图像实验结果进行分析比较,结果表明本文算法能够有效地抑制图像噪声、增强图像细节信息、改善图像视觉效果,可应用于多种图像增强领域.