Two low illuminance image enhancement algorithms based on grey level mapping

Two image enhancement contrast methods are proposed in this paper for low-intensity images. The first method (LEAM) is a new greyscale mapping function, and it can be significantly enhanced in the low grey range and compressed slowly in the high grey range, which is beneficial for retaining more image details; the second method (LEAAM) is based on the data characteristics of a histogram combined with the first mapping function, which adaptively sets the gamma value to correct the image. The experimental results show that compared with a traditional mapping function, LEAM is more effective at enriching image details and enhancing visual effects, and LEAAM, compared with a recent low-illumination image enhancement algorithm, achieves good performance for average gradient, information entropy and contrast index; additionally, the overall visual effect is the best compared with other methods.

     

The Retinex based improved underwater image enhancement

The underwater images suffer from low contrast and color distortion due to variable attenuation of light and nonuniform absorption of red, green and blue components. In this paper, we propose a Retinex-based underwater image enhancement approach. First, we perform underwater image enhancement using the contrast limited adaptive histogram equalization (CLAHE), which limits the noise and enhances the contrast of the dark components of the underwater image at the cost of blurring the visual information. Then, in order to restore the distorted colors, we perform the Retinex-based enhancement of the CLAHE processed image. Next, in order to restore the distorted edges and achieve smoothing of the blurred parts of image, we perform bilateral filtering on the Retinex processed image. In order to utilize the individual strengths of CLAHE, Retinex and bilateral filtering algorithms in a single framework, we determine the suitable parameter values. The qualitative and quantitative performance comparison with some of the existing approaches shows that the proposed approach achieves better enhancement of the underwater images.

     

Image contrast enhancement for preserving mean brightness without losing image features

Histogram equalization is a well-known and effective technique for improving the contrast of images. However, the traditional histogram equalization (HE) method usually results in extreme contrast enhancement, which causes an unnatural look and visual artifacts of the processed image. In this paper, we propose a novel histogram equalization method that is composed of an automatic histogram separation module and an intensity transformation module. First, the proposed histogram separation module is a combination of the proposed prompt multiple thresholding procedure and an optimum peak signal-to-noise ratio (PSNR) calculation to separate the histogram in small-scale detail. As the final step of the proposed process, the use of the intensity transformation module can enhance the image with complete brightness preservation for each generated sub-histogram. Experimental results show that the proposed method not only retains the shape features of the original histogram but also enhances the contrast effectively.     

自适应直觉模糊相异直方图裁剪的图像增强算法

针对经典的直方图均衡化图像增强算法可能存在的对比度过度增强、亮度分布不均匀和细节信息不突出等问题,提出自适应直觉模糊相异直方图裁剪的图像增强算法.基于直觉模糊集的“投票模型”,引入直觉模糊相异直方图的概念,并基于此提取图像像素的空间位置信息.同时,利用S型隶属度函数对图像直觉模糊相异直方图进行自适应裁剪,采用分段策略对裁剪后的直觉模糊相异直方图进行均衡化处理.最后,利用直觉模糊集的犹豫度刻画原图像的未知信息,修正由引导滤波获得的细节图像,从而保留图像丰富的细节信息.针对3种类型的图像,即自然图像、MRI脑图像及近红外图像的实验结果表明,所提出算法能够有效提高图像的对比度,保留图像的细节信息,使图像呈现较自然的视觉效果,改善图像的质量评价指标.     

Contrast enhancement for cephalometric images using wavelet-based modified adaptive histogram equalization

Cephalometric images usually have low contrast. The existing techniques for automatic cephalometric analysis usually use histogram equalization for image enhancement. This technique has the advantage of being fully automatic and nonlinear. However, it suffers from spikes, excessive enhancement, and lack of brightness preservation. The proposed technique is an adaptive histogram equalization technique that uses wavelet based gradient histograms. This paper compares its performance with two traditional techniques, three histogram modification based techniques, and two wavelet based techniques. Forty digital and scanned cephalograms are used to conduct tests. In addition to visual histograms and intensity profiles, the proposed method is compared in terms of eight quantitative measures. The various measures are applied to analyze the results in terms of contrast enhancement (EME, CNR), brightness preservation (AMBE), edge conservation and enhancement (H, TEN), preservation of image structures and non-addition distortion (MSSIM, SVD-M). The proposed method gives good contrast enhancement, with better brightness preservation without losing edge information and with the minimum addition of distortions to the enhanced cephalometric images.     

基于统计特征的图像直方图均衡化检测方法

直方图均衡化作为图像对比度增强技术之一,在图像恶意篡改过程中经常被作为隐藏被篡改图像强度变化的手段.本文利用图像直方图和其累积分布函数曲线,提取直方图均衡化的痕迹特征,实现直方图均衡化篡改检测.本文提出的方法首先利用图像直方图累积分布函数的变化趋势自适应地选择提取特征的灰度范围,然后在该范围内分别提取累积分布函数与恒等...     

结合暗通道原理和双边滤波的遥感图像增强

目的 在遥感应用如目视解译等任务中,需要提高遥感影像的视觉质量,为此提出一种基于暗通道原理和双边滤波的遥感图像增强算法。方法 由于暗通道模型的softmatting过程计算复杂性高,故使用双边滤波估计大气光幕,进而获得优化透射图,代替He算法中softmatting过程,提高了计算效率。针对将暗通道原理应用于遥感图像增强时所产生的色彩失真现象,提出透射图的改进算法,提高景深图像的取值,同时约束其最大值不大于1。最后,基于景深图像和暗通道原理获得增强后的遥感图像。结果 实验结果表明,本文算法能够有效地增加图像的对比度。与基于双边滤波单尺度Retinex图像增强、四尺度Retinex增强、直方图均衡化及MSRCR增强的结果进行了比较,实验结果验证了算法的有效性。结论 本文模型能够使处理后的遥感图像更符合视觉特性,以便于目视解译与分析。该算法适用于遥感图像的可视化增强。     

家装设计渲染图增强

目的 提出一种亮度、对比度、饱和度三要素与神经网络相结合的家装设计渲染图增强方法。方法 该方法分析了图像增强的3个要素：亮度、对比度和饱和度。算法从下列几个方面着手进行三要素的调节：1）根据原图饱和度和图像融合方法实现亮度和对比度增强;2）采用颜色矩阵实现饱和度增强;3）采用直方图均衡实现对比度进一步增强。这3个要素对图像增强的效果均有贡献,本文为三要素分别赋予一个权值,并引入神经网络方法,自动建立图像亮度分量均值、方差和饱和度分量均值、方差与三要素的权值系数的非线性映射关系。结果 根据图像本身的信息自动获取图像增强三要素的增强系数,实现家装设计渲染图的自适应增强。算法的有效性在不同程度偏灰暗的家装设计渲染图上得到了验证,并与几种经典方法进行了直方图、信息熵、平均对比度（AC）和平均灰度（AG）的定量比较。实验结果显示,本文算法实验结果的直方图具有很少的信息丢失和较好的特征保持,与遗传算法相比,信息熵提高了约0.2,AC值提高了约0.1,AG值提高了约15,本文算法在多数情况下评价指标优于改进的直方图方法。结论 通过对实验结果的直观评价与定量评价,证明与某些现有的方法相比,本文方法适用于不同程度偏灰暗的渲染图,具有较好的通用性,并能达到更优的渲染图像增强效果。     

基于直方图修正的局部对比度增强算法

针对传统直方图均衡化算法经常导致过增强,噪声放大和局部对比度低等缺点,提出了基于直方图修正的局部对比度增强算法。首先,利用自适应部分子块重叠方法把图像划分成一系列的子块,子块的大小能根据图像局部特征自适应调整;然后,对每个子块采用基于直方图修正的对比度增强方法进行处理;最后,融合各重叠子块的处理结果得到最终的增强图像。实验结果表明,该算法具有噪声鲁棒性、可控制增强等级、可调节动态范围和局部对比度较强的优势,增强后的图像具有更自然的视觉效果。     

Attacking contrast enhancement forensics in digital images

Currently, plenty of digital image forensic techniques have been proposed and used as diagnostic tools. It is urgent and significant to assess the reliability of such techniques applied in practical scenarios. In this paper, we investigate the security of existing digital image contrast enhancement （CE） forensic algorithms. From the standpoint of attackers, we propose two types of attacks, CE trace hiding attack and CE trace forging attack, which could invalidate the forensic detector and fabricate two types of forensic errors, respectively. The CE trace hiding attack is implemented by integrating loca] random dithering into the design of pixel value mapping. The CE trace forging attack is proposed by modifying the gray level histogram of a target pixel region to counterfeit peak/gap artifacts. Such trace forging attack is typically applied to create sophisticated composite images which could deceive the prior CE-based composition detectors. Extensive experimental results demonstrate the efficacy of our proposed CE anti-forensic schemes.     

深度视觉特征与策略互补融合的图像质量评价*

视觉多特征融合方法未考虑图像不同特征之间和不同评价算法之间的视觉互补性.通过融合人类视觉系统前端生理感知和后端心理处理特性,文中提出深度视觉特征互补融合(CPDVF)的图像质量评价方法.CPDVF深度提取图像的多通道直方图统计和多通道梯度结构这2种互补视觉特征,并进行深度视觉处理.然后设计局部失真度评价和局部相似度评价2种互补算法,分别对失真图像的上述互补视觉特征进行评价.最后联合视觉心理特性和回归函数,融合2种特征评价,获得失真图像质量的客观评价.实验表明,相比特征相似度、视觉显著等多特征联合方法,文中方法在准确度、单调性和可靠性指标上优势明显.     

Exact safety verification of hybrid systems using sums-of-squares representation

In this paper we discuss how to generate inductive invariants for safety verification of hybrid systems. A hybrid symbolic-numeric method is presented to compute inequality inductive invariants of the given systems. A numerical invariant of the given system can be obtained by solving a parameterized polynomial optimization problem via sum-of-squares （SOS） relaxation. And a method based on Gauss-Newton refinement and rational vector recovery is used to obtain the invariants with rational coefficients, which exactly satisfy the conditions of invariants. Several examples are given to illustrate our algorithm.     

Visual enhancement of underwater images using Empirical Mode Decomposition

Most underwater vehicles are nowadays equipped with vision sensors. However, it is very likely that underwater images captured using optic cameras have poor visual quality due to lighting conditions in real-life applications. In such cases it is useful to apply image enhancement methods to increase visual quality of the images as well as enhance interpretability and visibility. In this paper, an Empirical Mode Decomposition (EMD) based underwater image enhancement algorithm is presented for this purpose. In the proposed approach, initially each spectral component of an underwater image is decomposed into Intrinsic Mode Functions (IMFs) using EMD. Then the enhanced image is constructed by combining the IMFs of spectral channels with different weights in order to obtain an enhanced image with increased visual quality. The weight estimation process is carried out automatically using a genetic algorithm that computes the weights of IMFs so as to optimize the sum of the entropy and average gradient of the reconstructed image. It is shown that the proposed approach provides superior results compared to conventional methods such as contrast stretching and histogram equalizing.     

数字媒体图像多特征反差增强方法仿真

由于图像在生成的过程中会有相对的噪声产生,而噪声会直接影响该图像中的多特征信息,导致信息表达不完全等情况。基于此提出了数字媒体图像多特征反差增强方法。首先将需要对受噪声污染的图像进行去噪;其次运用偏微分方程(PDE)方法,构建出一种PDE数学模型-TV模型,在对图像去噪的同时,保持原图像的边缘轮廓信息,并且运用Split Bregman算法,并引入辅助变量转换出分段线性函数的泛函极值;在偏微分方程的基础上,运用改进后的反差增强方法,根据直方图的均衡化结果利用累积分布函数的方法,来当做图像灰度的变换函数,以达到合理的调整固定动态范围,且让图像整体视觉效果增强、提高图像中多特征信息的目的。通过仿真结果可以得知,提出的方法可以处理图像中噪声的影响,并且可以将图像中的多特征信息进行有效的反差增强处理,且具有直方图均衡化结果优秀、图像特征增强效率的优点。     

Image contrast enhancement for intelligent surveillance systems using multi-local histogram transformation

Among all applications to monitor the safety and security of working environments, surveillance systems that use computer vision are the most efficient and intuitive in the manufacturing industry. This paper introduces a new technique of contrast enhancement for surveillance systems using computer vision. The histogram equalization method is a common and widespread image enhancement method which maximizes the contrast of the image. This contrast enhancement method usually improves the quality of images, but it can suffer from visual deterioration caused by excessive histogram modification. To overcome the limitations of conventional contrast enhancement methods, this paper introduces a new multi-local histogram transformation method for surveillance systems. This technique is based on the local histograms, which are separated from the overall histogram of the image, and the contrast of the image can be enhanced through two major processes: range reassignment of local histograms and local histogram equalization. The multi-local histogram transformation in this paper enhances the contrast of images, preventing excessive compression and extension of image histograms. The performance of the suggested contrast enhancement method is verified by the experiments in four different environments.     

An effective and objective criterion for evaluating the performance of denoising filters

Denoising filters are useful for reducing noise; however, they often blur and smear the edges and boundaries, which are necessary for segmenting or locating the objects. In order to overcome above problem, many filters with contrast enhancement capability have been developed, and they have wide applications in related fields. Recently, researchers found that the traditional criteria, such as mean squared error (MSE), signal-to-noise ratio (SNR), are not suitable for evaluating such filters.Due to lack of effective metrics for such tasks, visual inspection by human and some newly proposed image quality assessment (QA) criteria, such as structural similarity (SSIM) index are utilized. However, visual inspection depends on the subjectivity of observers heavily.This paper has proved that evaluating denoising filters is different from image quality assessment, i.e., existing image quality assessment criteria cannot effectively evaluate the performance of denoising filters, especially, of the filters having contrast enhancement capability; and new criteria should be established. Further, it proposes a novel objective and effective assessment criterion, homogeneity mean difference (HMD), to evaluate the performance of the filters since it can describe the textual and structural information and/or the changes in textual and structural information well. We have employed 503 images from three databases to demonstrate the superiority of the proposed metric over the existing ones, and to prove that HMD is an effective and useful metric for assessing denoising filters with/without contrast enhancement, which may find wide applications in image processing and computer vision.     

一种新的自适应Lee图像增强算法

该文对Lee图像增强算法及其改进型算法进行了较深入的研究,发现了其存在的问题,提出了一种新的图像增强方法。该方法补充了一个有用的非线性变换,增加了原Lee算法的普适性;其次利用灰度值的统计特性达到了图像自适应增强的目的。实验结果表明:新算法不仅具有较大的适用范围,而且能够有效地增强整个图像的对比度,提高图像的视觉效果。     

基础信息保持和细节强化的胸部CT图像增强

目的 清晰的胸部计算机断层扫描(computed tomography, CT)图像有助于医生准确诊断肺部相关疾病,但受成像设备、条件等因素的限制,扫描得到的CT图像质量有时会不尽如人意。因此,本文提出一种简单有效的基于基础信息保持和细节强化的胸部CT图像增强算法。方法 利用多尺度引导滤波器将胸部CT图像分解为一个基础信息层和多个不同尺度的细节层。基于熵的权重将胸部CT图像的多个细节层进行融合,并乘以强化系数进一步增强纹理细节。将强化的细节层和原始的基础信息层重新组合即可生成细节强化的胸部CT图像。通过此种增强方式,本文算法既能显著增强胸部CT图像的纹理细节,又能将大部分原始的基础结构信息保留到增强图像中。结果 为了验证算法的有效性,将本文算法与5种优秀的图像增强算法在由3 209幅胸部CT图像组成的数据集上进行测试评估。定性和定量实验结果表明,本文算法得到的增强图像保持了更多原始胸部CT图像中的基础结构信息,并更显著地强化了其中的纹理细节信息。在定量结果中,本文算法的标准差、结构相似性和峰值信噪比指标值均优于对比的5种方法,相比于性能第2的方法分别提高了4.95、0.16和4.47,...     

A new non-symmetry and anti-packing model and its application to image contrast enhancement

In this paper, a non-symmetry and anti-packing image representation model (NAM) has been proposed. NAM is a hierarchical image representation method and it aims to provide faster operations and less storage requirement. By taking a rectangle sub-pattern, for example, we describe the idea of NAM and its encoding algorithm.In addition, an approach for adaptive area histogram equalization for image contrast enhancement based on a NAM image is presented. The contrast enhancement approach is designed to meet the NAM image representation and it can be duplicated with faster operation. The complexity analysis and the experimental results show that the NAM based algorithm for image contrast enhancement is faster and more effective than that based on matrix image.     

直方图重建图像细节增强算法

针对传统大动态范围图像数据压缩方法易受场景变化影响,量化后的8位显示图像整体模糊、图像细节和弱小目标丢失问题,提出了一种基于直方图重建图像细节增强算法。对图像直方图统计值进行重新赋值,保留图像中出现的细节部分,并缩小相邻灰度级间间隔;采用二维Gabor滤波器来模拟视觉感知系统,将Gabor滤波器与图像进行卷积运算,得到滤波后的平滑图像;采用局部对比度增强方法来增强图像细节部分,并将增强后的中间结果线性映射为8位显示图像。实验结果表明,与其他大动态范围数据压缩方法相比,该算法量化后图像清晰度高,无图像细节和目标丢失现象。