一种基于亮度直方图的自动曝光控制方法

自动曝光控制是现代相机和摄像机必备功能之一。提出一种基于亮度直方图的自动曝光控制算法。基于人类视觉系统(HVS)的视觉注意机制,可认为直方图中大而陡峭的峰值区域对应于图像中的不感兴趣区域。算法寻找直方图中最大的两个峰值区域,并依据峰值区域的大小确定它们中像素亮度的加权值,从而计算图像的加权亮度均值。像素亮度的加权值与其所属峰值区域的大小之间的关系由一组二次型曲线描述,二次型曲线的参量是由图像的背景亮度决定,其中背景亮度由自动曝光系统参量计算得到。在实际应用中采用了模糊逻辑确定最大的两个峰值区域的加权值。实验结果表明该算法对各种场景均能进行有效的自动曝光控制。     

二维直方图θ划分最大Shannon熵图像阈值分割

鉴于常用二维直方图区域直分法存在错分,最近提出的斜分法不具普遍性,提出了适用面更广的基于二维直方图θ划分和最大Shannon熵的图像阈值分割算法.首先给出了二维直方图θ划分方法,采用四条平行斜线及一条其法线与灰度级轴成θ角的直线划分二维直方图区域,按灰度级和邻域平均灰度级的加权和进行阈值分割,斜分法可视为该方法中θ=45°的特例;然后导出了二维直方图θ-划分最大Shannon熵阈值选取公式及其快速递推算法;最后给出了θ取不同值时的分割结果及运行时间,θ取较小值时,边界形状准确性较高,θ取较大值时,抗噪性较强,应用时可根据实际图像特点及需求合理选取θ的值.与常规二维直方图直分最大Shannon熵法相比,本文提出的方法所得分割结果更为准确,抵抗噪声更为稳健,且所需运行时间及存储空间也大为减小.     

基于梯度的区域生长和距离直方图的快速圆检测方法EI北大核心CSCD

针对基于Hough变换类圆检测算法所需设置参数较多和基于距离直方图的算法计算量大等问题,提出了一种基于梯度的区域生长和距离直方图的快速圆检测方法(GHC)。该算法通过利用梯度模值和方向进行区域生长的方法得到若干圆弧线段支撑区域;选取弧线段上的三个坐标点求解该圆弧段对应的圆心和半径并求解出正方形适应区域;将每条圆弧线段上的所有点向其适应区域内各坐标点进行投影并统计距离的累加值;综合全图距离直方图,精确地求解出图像中包含各圆的圆心和半径并进行完整度校验。通过实验表明,相比基于距离直方图的圆检测算法(HBCD)和随机Hough变换算法(RHT),该法对不同尺寸、完整度的单圆或多圆均有良好的检测效果,具有较强的稳健性和较小的空间、时间复杂度。     

一种基于修正直方图的图像增强算法

针对雾天降质图像对比度低的问题,提出了一种基于修正直方图的图像增强算法。统计图像的直方图时,每个像素分为两部分,一部分累加到当前像素灰度级,剩余部分按灰度级平均分配,实现直方图的修正,然后根据修正直方图产生灰度映射函数,由于每个像素只有一部分累加到当前灰度级,这样可以避免局部图像的过度增强。实验表明,该算法比经典的直方图均衡化算法、局部直方图均衡化算法有更好的增强效果。     

基于灰度冗余的红外图像自适应输出窗技术

根据红外图像的特点，提出了一种基于灰度冗余的红外图像自适应输出窗技术.该技术通过对图像灰度直方图的统计，设定适当的阈值，将灰度像素分布为零的灰度级进行完全压缩，将小于阈值的灰度级映射到阈值灰度级，然后将有效灰度级在整个可显示灰度级范围内作等间距排列.该算法在压缩灰度冗余的同时，实现了对有效图像灰度级的无损均衡，提高了图像质量，并能实现实时处理.     

一种改进的直方图均衡化

从理论上分析了直方图均衡化过程中灰度级合并的原因,给出了简单的物理解释和解析推导,并讨论了该算法的缺点。为了使图像保留更多的细节信息、消除虚假轮廓且保持连续性,提出了保留灰度级的直方图均衡化算法,并分析了其基本原理。实验结果表明:通过该算法处理的图像比采用传统直方图均衡化算法处理的图像效果好。     

基于改进的二维指数熵及混沌粒子群的阈值分割

鉴于现常用的灰度级—平均灰度级二维直方图区域划分存在明显的不足,提出了基于灰度级—梯度二维直方图的指数熵阈值选取方法,给出了基于改进的二维直方图的指数熵阈值选取公式,并利用混沌粒子群优化算法寻找最佳分割阈值,采用递推方式降低迭代过程中适应度函数的计算代价。实验结果表明,与现有的有关算法相比,该方法不仅使分割后的图像区域内部更均匀、边界形状更准确、特征细节更清晰,而且使计算效率及粒子群的收敛精度得到提高。     

一种水下对空成像图像增强算法

针对水下对空成像图像的低对比度增强问题,在对两种直方图均衡化技术详细分析的基础上,提出了一种改进的直方图均衡化的快速算法.该算法将图像划分为不同子区域,计算子区域的均衡化函数,然后设置移动子块的大小和移动步长,最后采用插值方法实现图像的平滑处理.该算法在较好地突出图像细节信息、消除块状效应的同时,避免了复杂的数学运算,取得了较好的效果.     

结合人眼视觉关注特性的视频质量评价

考虑到人眼视觉关注特性在视频质量评价(VQA)中所具有的重要作用,提出了一种结合人眼视觉关注特性的视频质量评价方法。首先利用三维Sobel算子以及恰可察觉失真模型得到全局显著图,对全局显著图的每个显著像素点构建结构张量来求取一帧的全局质量;然后利用视频运动信息以及人眼中心关注特性求得局部显著图来进行感知加权,得到一帧的局部质量;最后均衡局部与全局质量得到视频中一帧的质量,并采用机器学习的方法获得时域加权模型,对视频帧进行加权,从而得到客观视频质量评价值。在LIVE视频数据库上进行性能测试,得到PLCC(Pearson Linear Correlation Coefficient)为0.827,SROCC(Spearman Rank Order Correlation Coefficient)为0.802,与已有相关算法相比,所提出的VQA方法的评价结果更接近人眼的主观感知。     

基于视觉熵的视觉注意计算模型

提出了一种基于视觉熵的模拟生物视觉信息处理的视觉沣意计算模型,在使用图像的低层特征的基础上,充分考虑了视觉熵中人眼对图像信息的主观量度.该模型首先将图像分成4×4的图像块,分别计算每个图像块的均值、方差、幅度和亮度变化率;采用中央邻域差算子得到各图像的特征图,合并成一个显著图;计算该图中每个显著区域的视觉熵,将最显著Ⅸ域作为视觉注意的焦点,利用人类视觉注意的抑制返回机制,实现视觉注意焦点的转移,从而完成模拟人类视觉注意的整个过程.实验结果表明,该模型基本上能够模拟视觉注意的过程,而且复杂度较低,具有较高的实用价值.     

Image enhancement via Median-Mean Based Sub-Image-Clipped Histogram Equalization

This paper presents a robust contrast enhancement algorithm based on histogram equalization methods named Median-Mean Based Sub-Image-Clipped Histogram Equalization (MMSICHE). The proposed algorithm undergoes three steps: (i) The Median and Mean brightness values of the image are calculated. (ii) The histogram is clipped using a plateau limit set as the median of the occupied intensity. (iii) The clipped histogram is first bisected based on median intensity then further divided into four sub images based on individual mean intensity, subsequently performing histogram equalization for each sub image. This method achieves multi objective of preserving brightness as well as image information content (entropy) along with control over enhancement rate, which in turn suits for consumer electronics applications. This method avoids excessive enhancement and produces images with natural enhancement. The simulation results show that MMSICHE method outperforms other HE methods in terms of various image quality measures, i.e. average luminance, average information content (entropy), absolute mean brightness error (AMBE) and background gray level.     

Perceptual contrast enhancement with dynamic range adjustment

Recent years, although great efforts have been made to improve its performance, few Histogram equalization (HE) methods take human visual perception (HVP) into account explicitly. The human visual system (HVS) is more sensitive to edges than brightness. This paper proposes to take use of this nature intuitively and develops a perceptual contrast enhancement approach with dynamic range adjustment through histogram modification. The use of perceptual contrast connects the image enhancement problem with the HVS. To pre-condition the input image before the HE procedure is implemented, a perceptual contrast map (PCM) is constructed based on the modified Difference of Gaussian (DOG) algorithm. As a result, the contrast of the image is sharpened and high frequency noise is suppressed. A modified Clipped Histogram Equalization (CHE) is also developed which improves visual quality by automatically detecting the dynamic range of the image with improved perceptual contrast. Experimental results show that the new HE algorithm outperforms several state-of-the-art algorithms in improving perceptual contrast and enhancing details. In addition, the new algorithm is simple to implement, making it suitable for real-time applications.     

Improving histogram-based image contrast enhancement using gray-level information histogram with application to X-ray images

Many applications of histogram-based techniques for the purposes of image enhancement are well known. However, these techniques often fail to produce satisfactory results for a broad variety of low-contrast images (e.g., X-ray images). In this paper, we propose a new form of histogram for image contrast enhancement. The input image is first divided into several equal-sized regions according to the intensities of gradients, their corresponding statistical values of gray levels are then modified respectively, and finally the processed histogram for the whole image is obtained by the summation of all the weighted values of regions. The fundamental characteristic of this new form of histogram is that the amplitudes of its components can objectively reflect the contribution of the gray levels to the representation of image information. Accordingly, this new histogram is called gray-level information histogram. The performance of many histogram-based enhancement techniques might be improved dramatically using the proposed histogram. Testing on the X-ray images validates the effectiveness of the new histogram.     

Image contrast enhancement using adjacent-blocks-based modification for local histogram equalization

Infrared images usually have some non-ideal characteristics such as weak target-to-background contrast and strong noise. Because of these characteristics, it is necessary to apply the contrast enhancement algorithm to improve the visual quality of infrared images. Histogram equalization (HE) algorithm is a widely used contrast enhancement algorithm due to its effectiveness and simple implementation. But a drawback of HE algorithm is that the local contrast of an image cannot be equally enhanced. Local histogram equalization algorithms are proved to be the effective techniques for local image contrast enhancement. However, over-enhancement of noise and artifacts can be easily found in the local histogram equalization enhanced images. In this paper, a new contrast enhancement technique based on local histogram equalization algorithm is proposed to overcome the drawbacks mentioned above. The input images are segmented into three kinds of overlapped sub-blocks using the gradients of them. To overcome the over-enhancement effect, the histograms of these sub-blocks are then modified by adjacent sub-blocks. We pay more attention to improve the contrast of detail information while the brightness of the flat region in these sub-blocks is well preserved. It will be shown that the proposed algorithm outperforms other related algorithms by enhancing the local contrast without introducing over-enhancement effects and additional noise.     

Adaptive threshold selection for background removal in fringe projection profilometry

In fringe projection profilometry, background and shadow are inevitable in the image of an object, and must be identified and removed. In existing methods, it is nontrivial to determine a proper threshold to segment the background and shadow regions, especially when the gray-level histogram of the image is close to unimodal, and an improper threshold generally results in misclassification of the object and the background/shadow. In this paper, an adaptive threshold method is proposed to tackle the problem. Different from the existing automatic methods, the modulation-level histogram, instead of the gray-level histogram, of the image is employed to determine the threshold. Furthermore, a new weighting factor is proposed to improve Otsu's method to segment the image with a histogram close to unimodal, and the modulation difference of the object pixels and the background/shadow pixels is intensified significantly by the weighting factor. Moreover, the weighting factor is adaptive to the image. The proposed method outperforms existing methods either in accuracy, efficiency or automation. Experimental results are given to demonstrate the feasibility and effectiveness of the proposed method.     

Image Defogging Framework Using Segmentation and the Dark Channel Prior

Foggy images suffer from low contrast and poor visibility problem along with little color information of the scene. It is imperative to remove fog from images as a pre-processing step in computer vision. The Dark Channel Prior (DCP) technique is a very promising defogging technique due to excellent restoring results for images containing no homogeneous region. However, having a large homogeneous region such as sky region, the restored images suffer from color distortion and block effects. Thus, to overcome the limitation of DCP method, we introduce a framework which is based on sky and non-sky region segmentation and restoring sky and non-sky parts separately. Here, isolation of the sky and non-sky part is done by using a binary mask formulated by floodfill algorithm. The foggy sky part is restored by using Contrast Limited Adaptive Histogram Equalization (CLAHE) and non-sky part by modified DCP. The restored parts are blended together for the resultant image. The proposed method is evaluated using both synthetic and real world foggy images against state of the art techniques. The experimental result shows that our proposed method provides better entropy value than other stated techniques along with have better natural visual effects while consuming much lower processing time.     

基于优势特征图像融合的水下光学图像增强

针对水下光学图像颜色失真、非均匀光照、对比度低的问题,提出基于优势特征图像融合的水下光学图像增强算法.首先,提出改进的暗通道先验算法去除退化图像中的不均匀浑浊并均衡色彩;其次,对颜色校正图像分别使用基于加权分布的自适应伽玛校正算法和限制对比度自适应直方图均衡-同态滤波算法,增强颜色校正图像对比度并使其亮度均衡;最后,定义三幅融合图像即颜色校正图像、亮度均衡图像、对比度增强图像的关联权重图,通过多尺度融合算法获得融合图像.与单一预处理算法只能解决对应的退化现象相比,该算法对单幅退化图像进行多算法处理,得到三幅优势特征图像,通过不同权重的组合最大程度地将各优势特征相结合,得到的综合效果远超各单一算法优化效果,不再局限于解决颜色失真等单一问题.将本文算法与现有算法在主观评价和客观评价两方面进行实验对比,结果表明,该算法可以有效平衡水下图像的色度、饱和度及清晰度,视觉效果接近自然场景下的图像.     

一种可控的视频图像动态对比度增强算法

为增强显示设备进行视频信号显示时的图像对比度,提出了一种幅度可控的动态对比度增强算法。该算法利用一组归一化线性直方图数据和输入图像的归一化灰度直方图数据的结合,实现对输入图像的权重化直方图均衡化处理,进而达到幅度可控的动态对比度增强效果。该算法可以通过控制对比度增强幅度来避免传统直方图均衡化产生的过增强现象。将基于该算法的实时图像处理器应用于50英寸AC PDP上,实验结果表明,其对比度获得显著提升,并且能够根据外部接口的调节实现幅度可控的对比度增强效果。     

一种基于混合人工鱼群的图像模糊增强算法

模糊隶属度函数的形式直接影响灰度图像增强的质量。为进一步改善图像模糊增强的效果,对目前的模糊隶属度函数进行研究,并提出一种改进的参数化 型模糊隶属度函数用于图像增强。所提算法利用图像对比度的质量评价模型,结合人工鱼群算法和Powell算法搜索 型函数中的未知参数值,进而确定该模糊隶属度函数。通过实验结果表明：该算法能够较好的改善灰度图像质量,并且控制参数可通过优化算法自适应获得,具有较好的通用性,是一种有效的图像模糊增强算法。