A quantitative measure based infrared image enhancement algorithm using plateau histogram

A quantitative measure based scene-adaptive contrast enhancement algorithm for an infrared (IR) image is proposed. This method regulates the probability density function (PDF) of the raw image firstly, and then applies an improved plateau histogram equalization method whose plateau threshold is determined by the concavity of the regulated PDF to enhance the raw IR image. In the stepped parameter tuning process of the algorithm, quantitative measure EME is used as the criterion to determine the optimal PDF regulator factor and plateau threshold. The above improvements contribute to the performance promotion of the proposed algorithm, whose effectiveness is validated by the final assessment with visual quality and quantitative measures.     

Shell histogram equalization of color images

Histogram equalization (HE) is an effective technique for image enhancement. In this study, we devised a new technique called shell histogram equalization for color images. The technique is a dimensionality reduction method, which transforms 3-D space enhancement to 1-D shell enhancement. First, the 3-D RGB color space is decomposed into L (L = 256) RGB shells, which are similar to a quarter sphere shells or a quarter onion squamae. Then, HE is implemented on shells, and makes the shells coincide with the distribution of the iso-luminance-planes in the RGB cube. After analyzing the computational complexity of the proposed method, comparison experiments are carried out and validated by subjective and objective assessments. The experimental results show that the method provides better enhancement for underexposed and high dynamic range images, and the computational time of the method is much lower.     

基于动态直方图均匀化的对比度增强方法

为增强显示设备进行视频信号显示时的图像对比度,提出了一种动态直方图均匀化算法,该算法根据对比度增强系数可实现在不同灰度范围内的直方图均匀化,达到不同程度的对比度增强效果。同时为减少进行对比度增强所需的帧存储器,开发出了一种快速简单的输入视频图像帧间相似性检测方法及电路实现,当判定输入视频图像具有相似性时,后续帧的图像采用前面输入图像的对比度增强映射函数。仿真结果表明,提出的对比度增强方法能根据对比度增强系数实现不同程度的对比度增强,有效地提高了显示设备的图像显示质量。     

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。     

Image enhancement by adjusting the contrast of spatial frequencies

We demonstrate a brand-new method for image enhancement by adjusting the contrast of different spatial frequencies. Fine characteristics of an image are well enhanced with negligible side effects. This method is easy to implement owing to its simple optical basis.     

视觉注意机制在图像增强中的应用研究

将视觉注意机制引入到直方图构造中,并在此基础上提出了一种新的基于灰度级信息量直方图的图像增强算法.该算法利用Itti视觉注意计算模型对图像的显著性进行分析,获得全局显著图;然后,将全局显著图划分为若干等大的子区域,求取各子区域的平均显著值,并做归一化处理,得到子区域的加权统计系数;再将各子区域的灰度级加权统计值相加,得到灰度级信息量直方图;最后,依据直方图均衡化的映射函数,调整灰度级的动态范围.实验结果表明,该算法明显优于经典的GHE算法和AHE算法,具有满意的视觉效果.     

Contrast enhancement of optical coherence tomography images using least squares fitting and histogram matching

A method of contrast enhancement of optical coherence tomography (OCT) images based on least squares fitting and histogram matching is presented. Several different functions are adopted as the probability density functions of the gray levels to fit the normalized histogram of an OCT image and histogram matching is used to enhance the OCT image automatically. The effectiveness of the method is proved by the experimental results.     

Image enhancement by modified contrast-stretching manipulation

We demonstrate a modified contrast-stretching method to enhance a non-uniformly illuminated image. Low-frequency information of the image is processed by the conventional manipulation, while the high-frequency information is exaggerated by the log transformation. Characteristics in the picture are better enhanced compared to the traditional algorithm.     

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.     

范围限制的自适应亮度保持多阈值直方图均衡算法研究

针对目前直方图均衡算法难以实现,且易造成亮度饱和等问题,本文提出了一种范围限制的自适应亮度保持多阈值直方图均衡算法。首先,对输入图像进行适当平滑,从而获得它的直方图峰值点个数（N+1）。然后,对Otsu算法进行N阈值扩展,并通过这种方法获得图像的N个分割阈值,从而按照此阈值对图像进行分割。为了能够最大程度地保持输入图像的亮度,利用输入图像和输出图像的均值亮度最小误差（AMBE）准则,重新计算了图像的均衡范围。最后,利用新的均衡范围分别对每一个子图像进行均衡。实验表明,使用本算法处理Lena图的绝对均值亮度误差为0.416 4,明显优于使用RLBHE算法的0.629 5。本算法能够获得更清晰的图像细节,同时图像的整体亮度保持的也较好。     

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.     

Effective contrast enhancement method for color night vision

Image fusion refers to the techniques that integrate complementary information from multiple image sensors’ data in a way that makes the new images more suitable for human visual perception. The paper focuses on the low color contrast problem of linear fusion algorithms with color transfer method. Firstly, the contrast of infrared and visible images is enhanced using local histogram equalization and median filter. Then the two enhanced images are fused into the three components of a Lab image in terms of a simple linear fusion strategy. To enhance the color contrast between the target and the background, the scaling factor is introduced into the transferring equation in the b channel. Experimental results based on three different data sets show that the hot and cold targets are all popped out with intense colors while the background details present natural color appearance. Target detection experiments through target recognition area, detection rate, target-background discrimination also show that the presented method has a better performance than the former methods.     

基于灰度冗余图像融合增强研究

为了改善原图像直方图像素灰度分布集中于低灰度级,与经过常规直方图均衡化处理的图像直方图像素灰度分布集中于高灰度级且均具有灰度冗余的图像增强效果,提出了一种新的图像增强算法.对原始图像进行常规直方图均衡处理,并对原始图像与进行了常规直方图均衡处理的图像分别在尽可能大的范围内进行映射,使两者变换后都尽可能去除灰度冗余而有较大的动态范围.取变换后的两图像各自的优点,融合成一幅效果最好的图像.实验结果验证了理论分析的正确性.     

一种结合小波分析与直方图的红外图像增强方法

针对传统红外图像增强算法中存在的问题,提出一种结合小波分析与直方图的红外图像增强方法.采用正交小波变换对红外图像进行处理,得到小波各层的分解系数;运用双向直方图均衡法对红外图像的低频子带小波系数进行处理,利用阈值滤波的细节系数增强法对红外图像的高频子带小波系数进行处理,经小波逆变换图像重构得到增强后的红外图像.实验结果...     

Fast color contrast enhancement method for color night vision

The increasing availability and deployment of imaging sensors operating in multiple spectral bands has led to a large research effort in color image fusion, resulting in a plethora of pixel-level image fusion algorithms. In this study a simple and fast fusion approach for color night vision is presented. The contrast of infrared and visible images is adjusted by local histogram equalization. Then the two enhanced images are fused into the three components of a Lab image in terms of a simple linear fusion strategy. To obtain false color images possessing a natural day-time color appearance, this paper adopts an approach which transfers color from the reference to the fused images in a simplified Lab space. To enhance the contrast between the target and the background, a stretch factor is introduced into the transferring equation in the b channel. Experimental results based on three different data sets show that the hot targets are popped out with intense colors while the background details present natural color appearance. Target detection experiments also show that the presented method has a better performance than the former methods, owing to the target recognition area, detection rate, color distance and running time.     

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。     

多子直方图均衡微光图像增强及FPGA实现

针对微光图像对比度低,目标难以识别的问题,对微光图像增强算法进行了研究。提出了一种多子直方图均衡增强算法,该算法首先将直方图按面积平均分割成4个子直方图,利用平均像素数量作为阈值切割直方图降低过度增强现象,然后加入尺度因子对动态范围进行调整,最后分别对子直方图均衡得到增强效果。此算法用Verilog语言在现场可编程门阵列（FPGA）上具体实现,并给出了主观和客观的评价,改进算法能产生更清晰的图像,在硬件平台上也能实时显示增强效果,一帧图像处理时延约为0.45 ms。实验结果表明,改进算法不会产生饱和、噪声放大的现象,图像细节保持较好,满足视频图像处理实时性要求,得到了具有较好视觉效果的增强图像。     

Variational infrared image enhancement based on adaptive dual-threshold gradient field equalization

Infrared images are characterized by low signal to noise ratio (SNR) and fuzzy texture edges. This article introduces the variational infrared image enhancement algorithm based on gradient field equalization with adaptive dual thresholds. Firstly, we transform the image into gradient domain and get the gradient histogram. Then, we do the gradient histogram equalization. By setting adaptive dual thresholds to qualify the gradients, the image is prevented from over enhancement. The total variation (TV) model is adopted in the reconstruction of the enhanced image to suppress noise. It is shown from experimental results that the image edge details are significantly enhanced, and therefore the algorithm is qualified for enhancement of infrared images in different applications.     

Global brightness and local contrast adaptive enhancement for low illumination color image

An adaptive enhancement algorithm is proposed in the paper for low illumination color image which is low brightness and low contrast. The algorithm is realized through three steps: the global brightness adaptive adjustment, locally adaptive contrast enhancement and color restore. Experiments show that the brightness and local contrast of low illumination color image can be effectively enhanced. The details, especially in the dark region of enhanced image are more prominent and the enhanced image is more vivid than some other methods for low illumination color image enhancement.     

Image enhancement through contrast enlargement using the image regions extracted by multiscale top-hat by reconstruction

An image enhancement algorithm based on multiscale top-hat by reconstruction is proposed in this paper. Firstly, multiscale top-hat by reconstruction using multiscale structuring elements is discussed. Then, multiscale bright and black image regions are extracted. Thirdly, useful image regions for image enhancement are obtained from the extracted multiscale bright and black image regions. Finally, after a base image is calculated from the results of the opening and closing by reconstruction operations, the original image is enhanced through combing the useful image regions into the base image. Experimental results on different types of images show that the proposed algorithm is efficient.