HIS空间的火灾图像模糊增强快速算法

火灾图像增强是图像型火灾探测系统中的重要环节。为了克服传统模糊增强算法运算量大、耗时长等问题,采用建立3维查找表并改进模糊增强算子的方法,设计了1种基于颜色三要素空间的火灾图像模糊增强快速算法。首先将火灾图像由三原色空间转换到三要素空间,然后保留H分量,分别对I分量、S分量建立3维查找表,通过3维查找表对图像中所有像素点的I分量、S分量进行模糊增强操作,最后将图像转换回三原色空间。结果表明,该算法在保留火焰色彩信息的同时大大降低了运行时间。这一结果对火灾的实时性探测是有帮助的。     

基于自适应权重Retinex和小波变换的彩色图像增强算法

针对现有图像增强技术容易出现细节丢失、局部曝光不足、过曝光或颜色失真,不能兼顾对比度和色彩保真的问题,提出了基于自适应权重Retinex与小波变换结合的彩色图像增强算法(AMSR-WT)。将图像从RGB空间转换到HSI空间,对亮度分量I进行小波变换分解为低频亮度图像和若干高频亮度图像,对低频图像使用自适应权重Retinex进行增强,对高频图像使用改进的阈值去噪算法进行去噪,通过小波逆变换重构亮度分量,经过Gamma校正进一步增强对比度并转换回RGB空间得到增强图像。实验结果表明,该算法有效提高了图像对比度和颜色保真度,较好地保留了图像的细节和纹理。     

结合图像特征比的红外与可见光图像融合算法

针对现有图像融合算法得到的融合图像清晰度不高以及边缘模糊等问题,提出了一种结合图像特征比的红外与可见光的图像融合算法,通过小波变换将图像进行分解,并通过计算低频分量中空间频率比与能量比,来对低频分量中的有效信号进行保留,而对高频分量则是通过边缘检测算法计算出高频分量中的边缘信号比,来对高频分量的边缘信号进行保留;实验结果表,该算法能够得到相比其他算法更为清晰的融合图像,具有一定的实用价值。     

基于FCM的彩色车牌图像快速分割方法

提出了一种基于HSI空间的快速模糊C均值（FCM）分割算法,并将此算法应用于分割彩色车牌背景与字符区域。首先将彩色车牌图像从RGB空间转换到HSI空间,将亮度I的值量化到与色调H同一范围[0,360];然后将具有相同值（H,I）的像素点归为一个有效样本点;最后采用有效样本点（H,I）进行二维模糊C均值聚类。该算法中根据H和I分量的特点,修正了距离公式。实验结果表明,该算法能良好地分割彩色车牌的背景与字符区域,并且运算速度高于标准的FCM。     

低照度下具有动态特性的彩色图像质量优化算法

针对低照度环境下具有动态性的彩色图像辨识度相对较差的问题,提出了基于人类视觉感知模型的对比度分辨率补偿算法。首先,将图像从RGB空间转换到HSV空间,保持H分量不变;对V分量提取图像特征参数,然后对V分量进行对比度分辨率补偿,增强图像亮度;并对S分量进行线性拉伸,复原图像色彩信息;最后将处理后的V分量和S分量与H分量进行反变换生成RGB空间新图像。实验结果表明,该算法对低照度下具有动态性的彩色图像增强效果较好,较好地保持了图像的细节,取得了良好的视觉效果。     

基于模糊理论和CLAHE的雾天图像自适应清晰化算法

为了解决雾天图像低对比度的问题,提出了一种基于模糊理论和CLAHE的雾天图像的自适应清晰化算法.此算法结合图像的均值和标准差,将雾天图像从空域转换到模糊域,采用模糊增强算法实现全局雾天图像的自适应对比度增强后再采用有约束的局部直方图算法对雾天图像的亮度分量进行处理,在空域内进一步实现雾天图像的对比度增强.实验结果表明,该算法将模糊域和空间域的方法相结合,可以提高雾天图像的亮度和对比度,使雾天图像的视觉效果得到一定改善.     

改进Retinex与多图像融合算法用于低照度图像增强

为了解决低照度图像在图像增强过程中图像质量不佳、对比度不高等问题,本文提出改进Retinex与多图像融合算法用于低照度图像增强。首先将待处理图像转换到HSV色彩空间,并设定阈值对其V通道分量进行亮度调节,然后转换到RGB色彩空间,将其拷贝3份,对第一份进行直方图均衡化,中值滤波处理;对第2份进行自动亮度调节,双边滤波处理;对第3份进行改进的Retinex算法处理,采用高斯滤波、双边滤波作为其环绕函数,估计图像照明分量,最后输出反射图。将处理后的3份图像转到HSV色彩空间,对其V分量进行多图像融合,H、S分量沿用第2份图像分量值,最后将融合后的图像由HSV转为RGB色彩空间,输出处理后的图像。实验结果表明,本文提出的算法在增强低照度图像的同时,还可抑制图像噪声,同时具有良好的保边性,且细节明显。     

一种基于HSI颜色模型的车道线检测算法

为了解决目前车道线检测算法容易受环境影响且在某些情况下效果不佳的问题,提出一种基于HSI彩色颜色空间的车道线检测算法。首先,算法将RGB格式的图像通过公式转换为HSI格式;然后,对HSI颜色模型中的亮度分量I进行对比度增强,从而有效降低了光照的影响;其次,根据H、S、I各分量的特点结合OTSU法对每个分量进行处理,通过MATLAB获取各分量的最佳阈值;再次,将处理完的三个分量合并成RGB格式,以便于计算机进行存储;最后,根据车道线的灰度特点并结合数学形态学方法对车道线进行提取。实验结果表明,该算法在车道线检测方面具有一定的实用性。     

融合非物理模型的改进AM-RetinexNet图像增强算法

针对地下空间低照度图像色彩偏暗、亮度低且分布不均、增强后图像色偏和噪声高等问题，研究提出了融合非物理模型的改进AM-RetinexNet图像增强算法。该算法将RGB图像转换成HSV分量，利用HSV空间相互独立性实现图像亮度增强和色彩增强处理，其中S分量利用V分量提取的相关信息进行自适应调整，V分量采用融合直方均衡化与注意力机制优化的RetinexNet进行照度分量增强处理；将处理后HSV分量转化成RGB图像，并对图像进行自适应色彩恢复，得到照度增强图像。对比实验表明，在图像的细节处理、亮度整体增强处理、图像降噪和色彩视觉修正等方面该方法表现较好，测试指标中平均互信息(MI)、标准差(STD)、结构相似性(SSIM)、平均梯度(AG)、空间频率(SF)和峰值信噪比(PSNR)最佳，均值分别可达到6.18,70.62,0.56,13.29,36.53,39.22。     

基于卷积神经网络的室内低照度图像增强

针对现有的低照度图像增强算法复杂度过高,文章基于卷积神经网络,对比已有的MSRCR算法,进行改进,从而弥补MSRCR算法的不足(如不具有自适应性,设计参数过多)。本文将图像转换到HSI空间,提取出其亮度分量I,将该分量输入到卷积神经网络中,获得增强后的亮度分量I,再将其转回到RGB空间。本文所使用的所有训练集均为室内无光照图片,图片具有亮度均匀、整体亮度偏暗的特点。在处理这一图片时,本文算法结果图像有更好的峰值信噪比和彩色图像信息熵,拥有更好的视觉效果。     

Efficient edge-preserving algorithm for color contrast enhancement with application to color image segmentation

In this paper, a new and efficient edge-preserving algorithm is presented for color contrast enhancement in CIE Lu^′v^′ color space. The proposed algorithm not only can enhance the color contrast as the previous algorithm does, but also has an edge-preservation effect. In addition, the spurious edge points occurred due to the color contrast enhancement can be well reduced using the proposed algorithm. This is the first edge-preserving algorithm for color contrast enhancement in color space. Furthermore, a novel color image segmentation algorithm is presented to justify the edge-preservation benefit of the proposed color contrast enhancement algorithm. Based on some real images, experimental results demonstrate the advantages of color contrast enhancement, edge-preservation effect, and segmentation result in our proposed algorithm.     

Fast and Efficient Method for Fire Detection Using Image Processing

Conventional fire detection systems use physical sensors to detect fire. Chemical properties of particles in the air are acquired by sensors and are used by conventional fire detection systems to raise an alarm. However, this can also cause false alarms; for example, a person smoking in a room may trigger a typical fire alarm system. In order to manage false alarms of conventional fire detection systems, a computer vision‐based fire detection algorithm is proposed in this paper. The proposed fire detection algorithm consists of two main parts: fire color modeling and motion detection. The algorithm can be used in parallel with conventional fire detection systems to reduce false alarms. It can also be deployed as a stand‐alone system to detect fire by using video frames acquired through a video acquisition device. A novel fire color model is developed in CIE L*a*b* color space to identify fire pixels. The proposed fire color model is tested with ten diverse video sequences including different types of fire. The experimental results are quite encouraging in terms of correctly classifying fire pixels according to color information only. The overall fire detection system's performance is tested over a benchmark fire video database, and its performance is compared with the state‐of‐the‐art fire detection method.     

彩色图像特征空间变换的新算法及其应用

本文提出了一种彩色图像特征空间变换算法.该算法基于高维形象几何与仿生信息学理论,根据人眼特性,利用彩色图像中彩色信息的变化进行特征空间变换,能够提取出不受光源影响的图像特征.在彩色图像人脸检测应用中,与常见的肤色分割方法不同,该算法不需对肤色建模,不对彩色图像进行非线性彩色空间变换,而是直接将彩色原图变换到特征空间,在特征空间中定位平均脸特征数据的最佳匹配点.本文最后将该算法应用于复杂光源彩色图像的人脸检测中,实验结果验证了算法的有效性和鲁棒性,具有明显的应用价值.     

A fast and efficient color image enhancement method based on fuzzy-logic and histogram

A new fuzzy logic and histogram based algorithm for enhancing low contrast color images has been proposed here. The method is computationally fast compared to conventional and other advanced enhancement techniques. It is based on two important parameters M and K, where M is the average intensity value of the image, calculated from the histogram and K is the contrast intensification parameter. The given RGB image is converted into HSV color space to preserve the chromatic information contained in the original image. To enhance the image, only the V component is stretched under the control of the parameters M and K. The proposed method has been compared with conventional contrast enhancement techniques as well as with advanced algorithms. All the above techniques were based on the principle of transforming the skewed histogram of the original image into a uniform histogram. The performance of the different contrast enhancement algorithms are evaluated based on the visual quality, Tenengrad, CII and the computational time. The inter comparison of different techniques was carried out on different low contrast color images. Based on the performance analysis, we advocate that our proposed Fuzzy Logic method is well suited for contrast enhancement of low contrast color images.     

基于分数阶Retinex的低照度图像增强方法

低照度彩色图像增强在生活中起着重要作用,传统的低照度彩色图像增强算法往往会引起图像的不同程度失真。为了增强低照度彩色图像而又不引起图像失真,本文提出了一种新的低照度图像自适应对比度增强算法。将分数阶微积分、传统Retinex变分法与分段对数变换饱和度增强法相结合,构造一种新的分数阶Retinex图像增强算法。实验结果表明,该方法具有增强图像对比度的同时又能保持边缘和纹理细节的能力。与传统低照度图像增强算法相比,能突出图像的细节纹理信息,同时图像色度和亮度也有明显改善。     

Image quality assessment based on Prewitt magnitude

The goal of image quality assessment (IQA) research is to use computational models to calculate the quality of images consistently with subjective evaluations. In this paper, we propose a new image quality assessment (IQA) algorithm by combining Prewitt magnitude and regional mutual information (RMI) in HSV color space. The Prewitt operator is usually used for edge detection and can extract vertical edge more accurately than other operators. The HSV color space encapsulates information about a color in terms that are more natural and intuitive to humans. The proposed method PMRMI first transforms reference and distorted images from RGB color space into HSV color space and Prewitt magnitude is introduced to extract key edge features of each channel. Then the regional mutual information is calculated to measure the similarity of the two images. After that, a weighting method is utilized for better consistency with subjective evaluations. Therefore we get a single quality score. Experiments on various image distortion types demonstrate that the proposed algorithm can achieve better consistency with the subjective evaluations than PSNR and SSIM.     

基于FPGA的LED屏彩色图像增强算法设计

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。但是由于这种方法需要进行色彩空间的相互转化,大大增加了计算量。文章通过对简化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。FPGA可用来并行实时处理海量的图像数据,文章利用FPGA的这一特点作为实时图像直方图均衡增强算法的解决方案,并通过改进直方图均衡图像增强算法使其在FPGA中具体实现,可用于实时性要求高的LED屏显示。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。     

基于视觉特性的多尺度彩色图像增强算法

本文根据人眼对亮度和颜色不同的敏感特性，按照视觉心理特性选择了适当的色度空间，在此空间中提出了一种基于小波变换的彩色图像增强算法。和目前存在的灰度图像增强算法不同，不仅对亮度分量做出了动态范围调节，同时对图像的颜色分量也进行处理。实验证明，增强后的彩色图像不仅更加清晰，更加生动，鲜艳，达到了预期的目标。     

Look-up table (LUT) method for inverse halftoning

In this paper we propose look-up table (LUT) based methods for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. The method is extremely fast (no filtering is required) and the PSNR and visual image quality achieved is comparable to the best methods known for inverse halftoning. The LUT inverse halftoning method does not depend on the specific properties of the halftoning method, and can be applied to any halftoning method. Then, an algorithm for template selection for LUT inverse halftoning is introduced. We demonstrate the performance of the LUT inverse halftoning algorithm on error diffused images and ordered dithered images. We also extend LUT inverse halftoning to color halftones.     

基于颜色对比度增强的红外与微光图像彩色融合方法

针对用传统的颜色传递算法得到的彩色融合图像 存在目标与背景颜色相近、目标变淡的问题,提出了一种基于颜色对比度 增强的红外与微光图像彩色融合方法。首先对红外和微光图像进行 了伪彩色融合,然后对彩色参考图像与伪彩色融合图像进行了颜色传 递。其次,采用大津分割法分割出了微光图像中的目标信息,并得到了目标 区域。最后,在HSI颜色空间中利用目标区域对颜色传递后的融合图像 的H、S、I分量进行了调整,并得到了最终的融合图像。实验结果表明,利 用该方法得到的彩色融合图像具有目标与背景对比度高、细节丰富、色 彩较好等特点,可提高人眼对场景的理解和对目标的快速识别。