An effective local contrast enhancement technique by blending of local statistics and Genetic Algorithm

Nowadays, Image enhancement finds enormous image processing applications, which are related to practical situations, Contrast enhancement is one among the different image enhancement techniques that intends to improve the image visibility. Though several works for local contrast enhancement are available in the literature, the effectiveness remains an issue and the enhancement performance needs to be improved. In this paper, a local contrast enhancement technique is proposed for both gray scale images and RGB color images. The proposed technique is comprised of two stages of enhancement, namely, local statistics-based image enhancement and Genetic Algorithm based local contrast enhancement. The former stage is a pre-enhancement stage and the later is the major stage of enhancement. In the former stage, the image is processed in window basis and the local statistics of the image is obtained. Based on the local statistics, the image is enhanced. In the later stage, the window based operation is performed over the preenhanced image and the local contrast is enhanced. The Genetic Algorithm aids in searching of an optimal contrast factor, which plays vital role in the contrast enhancement. The technique is evaluated with both gray scale images as well as RGB color images and performance is compared with the existing contrast enhancement techniques.     

一种新的红外图像复合增强算法

通过对现有图像增强算法的分析,提出了一种新的红外图像复合增强算法。该算法将同态增晰和直接对比度增强算法结合,使得图像中的局部信息与全局信息在增强时都能被利用。外场实验表明,处理后的图像具有明显的局部对比度增强效果,同时较好地保持了图像的原始面貌。     

Contrast Enhancement and Intensity Preservation for Gray-Level Images Using Multiobjective Particle Swarm Optimization

The contrast enhancement of gray-level digital images is considered in this paper. In particular, the mean image intensity is preserved while the contrast is enhanced. This provides better viewing consistence and effectiveness. The contrast enhancement is achieved by maximizing the information content carried in the image via a continuous intensity transform function. The preservation of image intensity is obtained by applying gamma-correction on the images. Since there is always a trade-off between the requirements for the enhancement of contrast and preservation of intensity, an improved multiobjective particle swarm optimization procedure is proposed to resolve this contradiction, making use of its flexible algorithmic structure. The effectiveness of the proposed approach is illustrated by a number of images including the benchmarks and an image sequence captured from a mobile robot in an indoor environment.     

An image contrast enhancement algorithm for grayscale images using particle swarm optimization

This paper addresses a contrast enhancement technique that combines classical contrast enhancement with an evolutionary approach. The central goal of this work is to increase the information content and enhance the details of an image using an adaptive gamma correction technique aided by particle swarm optimization. Gamma correction is a well established technique that preserves the mean brightness of an image that produces natural looking images by the choice of an optimal gamma value. Here, Swarm intelligence based particle swarm optimization is employed to estimate an optimal gamma value. In the proposed method, the edge and information content (entropy) are the parameters used to formulate the fitness function. The proposed method is compared with state-of-the-art of techniques in terms of Weighted Average Peak Signal to Noise Ratio (WPSNR), Contrast, Homogeneity, Contrast Noise Ratio (CNR), and Measure of Enhancement (EME). Simulation results demonstrate that the proposed particle swarm optimization based contrast enhancement method improves the overall image contrast and enriches the information present in the image. In comparison to other contrast enhancement techniques, the proposed method brings out the hidden details of an image and is more suitable for applications in satellite imaging and night vision.     

一种基于局部方差的雾天图像增强方法

提出了一种基于图像局部方差的保持灰度级和亮度的雾天图像增强算法。即在对图像作局部处理的基础上，利用局部方差可以较好的体现图像细节信息的特性，通过计算并比较图像局部标准方差的大小来判断局部图像的增强程度，然后以灰度均值为基准进行局部灰度拉伸。该算法可以有效增强和保留图像细节，克服了传统直方图均衡化处理所造成的灰度级损失的缺点，保持了原图的灰度级和平均亮度，较好的抑制了噪声，图像视觉效果明显改善，因此特别适合于深度信息多变且对比度较低的雾天图像。实验结果表明该算法可以有效的增强雾天图像。     

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。     

Measurement of contrast enhancement in VDU screens

Contrast enhancement has been measured on three different VDUs with each of four different enhancement filters. The contrast enhancement filters are of different types — circular polarizing, mesh, neutral isotropically absorbing, and spectrally selective isotropic absorbing. For situations where the contrast reduction is caused by near-specular reflection of ambient light, the circular polarizing filter is considerably more effective than the others. For situations where the contrast reduction is caused by non-specular reflection of ambient light, the filters were all about equally effective. Thus, in an average situation where contrast of the unfiltered VDU image is reduced by both near-specular and non-specular reflections, it would be expected that the circular polarizer would enhance contrast to a noticeably greater extent than the other filters. Resolution was also measured through each of the filters which had been tested for contrast enhancement. The mesh filter noticeably degraded resolution, whereas the circular polarizer and the isotropically absorbing filters did not.     

Adaptive tone mapping for display enhancement under ambient light using constrained optimization

We propose adaptive tone mapping for display enhancement under ambient light using constrained optimization. To deal with the visibility reduction caused by ambient light in displays, we perform different operations for display enhancement according to the intensity of ambient light. Since weak ambient light has little effect on displayed images, we only perform contrast enhancement for them. However, strong ambient light makes displayed images dark for human eyes, causing severe visibility reduction in luminance and contrast. To enhance the visibility of displayed images under strong ambient light, we formulate a constraint optimization problem which consists of luminance enhancement, contrast enhancement, and distortion minimization terms, and find an optimal trade-off among them by solving it. Finally, we conduct color scaling to reproduce vivid colors in displayed images. Experimental results demonstrate that the proposed method significantly enhances the brightness, contrast, details, and colors of displayed images and outperforms other state-of-the-art methods under ambient light.     

A real-time image processor with combining dynamic contrast ratio enhancement and inverse gamma correction for PDP

Inverse gamma correction must be performed before displaying the received video signal because alternating current plasma display panel (AC PDP) has a linear output luminance response to a digital-valued input. At the same time contrast ratio enhancement is necessary for improving the image quality of display devices. The histogram equalization (HE) is an important contrast ratio enhancement method. But sometimes HE can produce unrealistic effects in images. In this paper, a new method of combining dynamic contrast ratio enhancement and inverse gamma correction for AC PDP is proposed. The dynamic contrast ratio enhancement and the inverse gamma correction are realized simultaneously in the proposed method. Furthermore the over-enhancement caused by the traditional HE can be avoided. A real-time image processor with the proposed method was designed and implemented. Simulations and experimental results on a 50-in. AC PDP show that the image quality of AC PDP can be improved obviously.     

图像增强算法与评价方法研究

研究了适应于乳腺钼靶X线图像特点的图像增强算法,探索量化评价增强算法效果的指标和体系。应用边界、标准偏差、熵以及4阶矩定义灰度均匀度,提出了增强放大因子来调节对比度增强程度的策略,同时给出了基于均匀度实现图像增强的算法,提出了4个增强效果量化评价指标。并与已实现的数学形态学增强算法和直方图均衡化算法进行了量化对比分析。综合分析指标表明,该算法优于数学形态学和直方图均衡化算法,但需要大量的实验和对比分析,研究了DSM、TBCs、TBCe和综合指标之间的相关性,从而找到了更适合于乳腺钼靶X线图像的对比度增强算法,探索出了更加科学的评价体系和指标。     

An efficient contrast-enhancement method using the analog to digital converter

The study presents a hybrid method of contrast enhancement using the analog to digital converter (ADC) for use in high-speed industrial applications. The proposed framework processes the digitized image in efficient steps to generate the low and high offsets of the ADC. These offsets are used to modify the analog image signal by an affine transform, such that the digitization of the transformed signalis the enhanced image. Since the signal transformation is mostly done in the analog domain, the method is efficient, and its implementation is inexpensive. As an example, the enhancement method is used in applications where, given images of varying initial contrast, we can obtain an user-specified contrast for all images. These images are then segmented by a simpler image-processing algorithm. The method is a generalization of existing ADC-based enhancement methods and is compared experimentally to the digital domain techniques. An application of the method is given for the enhancement of laser-etched characters on industrial parts.     

Human visual system-based image enhancement and logarithmic contrast measure.

Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.     

人工鱼群算法在自适应图像增强中的应用

图像增强是图像处理中的一个经典问题,以传统的分段线性变换为基础,利用人工鱼群算法和二维Otsu阈值法,提出了一种自适应的图像对比度增强算法。该方法利用优化后的人工鱼群算法自动选取双阈值,依据图像的对比度自动搜索灰度变换斜率,得到最优的分段线性变换曲线,并用之对图像进行增强处理。实验表明,该方法可有效提高图像对比度,且优于直方图均衡化、反锐化掩模等传统的对比度增强方法。     

一种基于图像方向信息测度算法的自适应表格图像增强算法

图像增强是图像处理的重要任务.为了有效地增强对比度极低的表格图像,提出了基于图像方向信息测度算法的自适应图像增强算法.该方法首先在像素级利用图像方向信息侧度将图像像素分为平滑点和阶跃边缘点;然后针对表格图像特点,再对平滑点细分为平滑点和屋脊边缘点,同时,对不同类型的点设计不同的滤波方法,以便通过算法自适应来初步增强图像;最后,利用模糊增强算法来对图像进行对比度增强,以达到更好的增强效果.实验表明,该算法能有效地增强对比度极低的表格图像,并使得结果图像有很好的视觉效果.     

An adaptive enhancement algorithm based on visual saliency for low illumination images

Applied Intelligence - In order to improve the brightness and contrast of low illumination color images and avoid over enhancement, an adaptive image enhancement algorithm based on visual saliency...     

基于改进蛙跳算法的图像对比度增强方法

为改善图像对比度,提出了一种基于改进蛙跳算法的图像对比度增强新方法。该方法利用分段线性变换增强图像的原理,对蛙跳算法进行深入分析并进行改进后,将改进蛙跳算法与二维Otsu法相结合,利用蛙群的并行搜索机制自动选取图像的双阈值,然后再以图像的对比度作为改进蛙跳算法的适应度函数,自适应地搜索分段线性变换的斜率,并以之增强图像。实验结果表明,该方法有效改善原图像的对比度,且优于直方图均衡化法及基于基本蛙跳算法和人工鱼群算法的增强方法。     

基于边缘检测的对比度增强法

该文主要介绍了图像增强方法及其分类,详细介绍了基于边缘检测的对比度增强法原理,其主要思想是将像素的边缘度结合到局部对比度定义中,因此更有利于边缘细节的对比度增强。     

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.     

基于纹理度划分的医学图像可逆信息隐藏方法

针对目前具有对比度增强效果的可逆信息隐藏（RDH）算法大多受到嵌入率的影响,未能实现较好的对比度增强效果的问题,提出一种基于纹理度划分的医学图像可逆信息隐藏方法。首先,通过对比度拉伸的方法实现医学图像对比度增强;然后,再利用医学图像纹理度的自身特点,将医学图像划分为高、低两类纹理度等级,其中高纹理度等级构成医学图像的关键部分;最后,为了进一步增强高纹理度等级的对比度并保证信息嵌入率,对不同的纹理度等级像素采用不同的嵌入方法。为了与其他具有对比度增强效果的医学图像可逆信息隐藏算法比较载密图像的对比度增强效果,采用了针对对比度失真的无参考图像质量评价标准（NR-CDIQA）作为实验客观依据。实验结果表明,在不同嵌入率下,使用所提方法的载密图像的NR-CDIQA值更高,对比度增强效果更好。     

一种基于视觉特性的彩色图像增强算法

为了增强图像暗区域部分,基于人类视觉系统的全局和局部自适应调节原理,提出一种彩色图像增强方法。该方法主要包括全局自适应亮度调节、局部对比度增强和颜色恢复3个部分。全局亮度调节采用直方图非线性自适应拉伸来增强暗区域的亮度;局部对比度增强利用当前点与区域像素之间的关系,调节当前点的亮度,以增强图像局部对比度;通过一种自适应的非线性颜色恢复算法恢复图像色彩。通过大量图像对比实验分析表明,本文方法可以自适应有效快速地实现图像增强。