Cuckoo search with search strategies and proper objective function for brightness preserving image enhancement

Image enhancement can be formulated as an optimization problem where one parameterized transformation function is used for enhancement purpose. The proper enhancement significantly depends on two factors- fine tuning of the parameters of the corresponding parameterized transformation function and other one is the selection of a proper objective function. In this study a parameterized variant of histogram equalization (HE) has been used for enhancement purpose and to tune the parameters of that variant a modified cuckoo search (CS) with new global and local search strategies is employed. This paper also concentrates on the selection of a proper objective function to preserve the original brightness of the image. A new objective function has been developed by combining fractal dimension (FD) and quality index based on local variance (QILV). Visual analysis and experimental results prove that modified CS with search strategies outperforms the traditional and some other existing modified CS algorithms. Considering the image’s brightness preserving capability, the proposed objective function significantly outperforms other existing objective functions.     

Artificial Bee Colony-based satellite image contrast and brightness enhancement technique using DWT-SVD

In this article, a new contrast enhancement approach is presented for quality enhancement of low-contrast satellite images. The proposed technique is based on the Artificial Bee Colony (ABC) algorithm using Discrete Wavelet Transform and Singular Value Decomposition (DWT-SVD). The method employs the ABC technique to learn the parameters of the adaptive thresholding function required for optimum enhancement. In this approach, the input image is primarily decomposed into four sub-bands through DWT, and then each sub-band of DWT is optimized through the ABC algorithm. After that, a singular value matrix of the low–low thresholded sub-band image is estimated and, finally, the enhanced image is constructed by applying inverse DWT. The results obtained through this method reveal that the proposed methodology gives better performance in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), and mean and standard deviation as compared to General Histogram Equalization (GHE), Discrete Cosine Transform and Singular Value Decomposition (DCT-SVD), DWT-SVD, Particle Swarm Optimization (PSO), and modified versions of the PSO-based enhancement approach.     

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。     

一种基于雾天图像增强的SURF图像匹配方法

在地质灾害监测系统中,可以通过图像的变化检测对发生的灾害进行监测。要精确地进行变化检测,需要首先对图像进行匹配。针对雾天图像匹配精度低的问题,提出了一种基于雾天图像增强的SURF（Speed Up Robust Feature）图像匹配方法,有效地提高了雾天图像的匹配精度。用MSR（Multi Scale Retinex）算法对雾天图像进行增强处理,采用SURF算法完成特征点的提取,用欧式距离来度量特征点的相似度,根据相似三角形的距离比例不变性删除误匹配的点对。实验结果表明,该方法显著提高了图像特征点的匹配精度,为后续利用图像变化检测进行灾害监测提供了良好的基础。     

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。     

Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified Differential Evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no-reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend.     

用于实现图像对比度增强的可逆数据隐藏算法

针对已有算法嵌入率低、算法过程复杂等问题,提出一种新的可逆数据隐藏算法。算法为了提高信息隐藏率,首先选取直方图中值点两侧的峰值进行数据嵌入。在多次嵌入之后,算法选择中值点位置嵌入所有峰值信息,由于中值点在嵌入后可以直接定位,因此可以实现可逆嵌入与恢复,从而不需要已有算法最低有效位的替换过程,减少了算法过程的复杂性。算法在两个图像集上进行性能测试,先与已有算法进行数据嵌入量比较,接着与MATLAB中3个对比度增强函数进行比较,用于评估算法的嵌入容量以及对比度增强后的图像质量。实验结果表明,所提算法在实现可逆数据隐藏和对比度增强的同时,嵌入容量有较大的提高。     

一种新的基于相位保持的指纹图像增强算法

图像的相位信息在人眼感知图像的过程中起着十分重要的作用。如果指纹图像增强算法能够保证图像的相位信息不被破坏,将有助于保持指纹图像中纹理特征的不变。首先通过实验表明了指纹图像的纹理特征主要是由图像的相位信息来决定的,然后把相位保持原理引入到指纹图像增强算法中,提出一种新的基于相位保持的指纹图像增强算法。该算法采用复Gabor小波来提取指纹图像的相位信息,并利用指纹图像的局部方向信息在相位保持的原则下来进行图像增强。为了验证该算法的有效性,选取了两种低质量的指纹图像来进行处理,实验结果表明增强后的图像其图像质量有了显著的提高。     

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.     

A fuzzy histogram weighting method for efficient image contrast enhancement

Multimedia Tools and Applications - Image contrast enhancement is an important step in digital image processing applications. In this paper, we present an efficient contrast enhancement approach,...     

Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement

Multimedia Tools and Applications - Image enhancement remains an intricate problem, crucial for image analysis. Several algorithms exist for the same. A few among these algorithms categorize images...     

用于实现医学图像ROI对比度增强的可逆数据隐藏算法

基于可逆数据隐藏技术具有可以准确地提取嵌入的秘密消息并无损还原原始载体的特点,可逆数据隐藏技术被广泛使用在医学和军事等领域。提出一种新的基于直方图平移的可逆数据隐藏算法,针对医学图像的像素分布特点,利用最大类间方差法分离背景和前景区域,确定边缘像素。对已有算法的预处理过程进行改进,使其能够自适应地选择最优预处理区间,降低视觉失真,并在信息嵌入的同时使医学图像感兴趣区域的对比度增强效果得到逐步改善,最终原始医学图像可以无失真地恢复。与现在已知的方法相比,新算法可以使医学图像ROI的对比度增强效果得到更明显的改善,大幅度提高了图像的视觉质量。     

基于线性模型的图像对比度增强取证

针对当前全局对比度增强检测算法在中低强度JPEG压缩质量因子下分类精度不高的问题,文章提出了一种基于线性模型的图像对比度增强检测算法。文章首先提出从图像中提取图像噪声残差,对图像噪声残差采取分块策略计算每块的线性模型,根据对应图像块的线性模型计算相应的功率谱密度来呈现线性模型的特性。然后,计算整幅图像的均值功率谱密度作为分类特征。最后,采用支持向量机进行分类。实验结果表明,此方案能够有效地分类对比度增强图像,而且能够抵抗图像的JPEG压缩。     

A new image contrast enhancement technique based on a contrast discrimination model

A new image contrast enhancement technique is presented. It is based on experimental studies of contrast discrimination of objects in a background of variable brightness. A mathematical model that follows a power law has been developed. Comparisons between the proposed technique and both of the histogram equalization and histogram hyperbolization techniques have been made. Tests on different images show that the new method presents better results.     

An adaptive dynamic range compression with local contrast enhancement algorithm for real-time color image enhancement

Journal of Real-Time Image Processing - Dynamic range compression has become an important function used in modern digital video cameras to improve visual quality of color images suffered from low...     

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.     

基于自商图像的人脸图像增强

为了降低光照对人脸识别效果的影响,通过多层次自商图像的方法获得反射系数。将原图作为输入图像采用反锐化掩模滤波的方式进行增强,再通过自商图像方法获得反射系数和光照成分;把光照成分作为输入图像,重复以上操作,对每次得到的反射系数按比例融合得到最终的反射系数。YaleB人脸库上的实验结果表明,该方法能在一定程度上去除光照的影响,使人脸识别率有一定的提高。     

自适应双向保带宽对数变换及低照度图像增强

目的 在低照度环境下,由于受图像采集设备的限制,导致获取到的图像往往亮度低、对比度差。针对这一问题,提出一种自适应双向保带宽对数变换的增强算法。方法 首先通过标准化变换将低照度图像处理成标准化图像,然后根据标准化图像的平均亮度进行自适应双向保带宽对数变换,最后对图像取整输出,从而得到增强后的图像。结果 实验选用LIVE database release2标准库中29幅高质量图像作为参考图像,然后经Photoshop CS5统一处理成低照度图像,使用本文算法对其增强,并与直方图均衡化（HE）、多尺度Retinex增强（MSR）、自然保持的增强算法（NPEA）的结果进行比较。本文算法增强后的图像其整体对比度和亮度在主观上都有较大提高,增强效果优于其他3种方法;同时,本文算法峰值信噪比（PSNR）和结构相似度（SSIM）客观指标平均值分别为22.75和0.86,明显高于其他3种算法。另外,在算法运行效率方面,本文算法平均运行时间也较短,约为74 ms。结论 本文算法增强后的图像更自然、更符合人眼视觉特性,且算法简单易于实现,运行效率高。该算法广泛适用于背光或光照不均的低照度环境下的图像增强。     

Multichannel image contrast enhancement based on linguistic rule-based intensificators

This study follows the direct approach to image contrast enhancement, which changes the image contrast at each its pixel and is more effective than the indirect approach that deals with image histograms. However, there are only few studies following the direct approach because, by its nature, it is very complex. Additionally, it is difficult to develop an effective method since it is required to keep a balance in maintaining local and global image features while changing the contrast at each individual pixel. Moreover, raw images obtained from many sources randomly influenced by many external factors can be considered as fuzzy uncertain data. In this context, we propose a novel method to apply and immediately handle expert fuzzy linguistic knowledge of image contrast enhancement to simulate human capability in using natural language. The formalism developed in the study is based on hedge algebras considered as a theory, which can immediately handle linguistic words of variables. This allows the proposed method to produce an image contrast intensificator from a given expert linguistic rule base. A technique to preserve global as well as local image features is proposed based on a fuzzy clustering method, which is applied for the first time in this field to reveal region image features of raw images. The projections of the obtained clusters on each channel are suitably aggregated to produce a new channel image considered as input of the pixelwise defined operators proposed in this study. Many experiments are performed to demonstrate the effect of the proposed method versus the counterparts considered.