Contrast Limited Adaptive Histogram Equalization for Qualitative Enhancement of Myocardial Perfusion Images

This paper establishes an efficient color space for the contrast enhancement of myocardial perfusion images. The effects of histogram equalization and contrast limited adaptive histogram equalization are investigated and the one which gives good enhancement results is extended to the suitable color space. The color space which gives better results is chosen experimentally. Uniqueness of this work is that contrast limited adaptive histogram equalization technique is applied to the chrominance channels of the cardiac nuclear image, leaving the luminance channel unaffected which results in an enhanced image output in color space.

Morphological Filters and Wavelet-based Histogram Equalization Image Enhancement for Weak Target Detection

Image enhancement methods are typically aimed at improvement of the overall visibility of features. Though histogram equalization can enhance the contrast by redistributing the gray levels, it has the drawback that it reduces the information in the processed image. In this paper, we present a new image enhancement algorithm. After histogram equalization is carried out, morphological filters and wavelet-based enhancement algorithm is used to clean out the unwanted details and further enhance the image and compensate for the information loss during histogram equalization. Experimental results show that the morphological filters and wavelet-based histogram equalization algorithm can significantly enhance the contrast and increase the information entropy of the image.

An Improved Histogram Equalization Method in the Traffic Monitoring Image Processing Field

To process the traffic monitoring image, a local Histogram Equalization method based on fuzzy mathematics was proposed in this paper. In this paper, firstly, we define a function to measure the similarity degree of two images. Then, a suitable Gaussian fuzzy distribution function was chose to generate a 3 × 3 matrix of influential factors. In order to reduce the artificial boundaries, we combined the 3 × 3 influential matrix with a 3 × 3 smooth filter matrix to get the final smooth-influ- ence matrix. Finally, the smooth-influence matrix was used to process the center block image. The simulation results demonstrated that the proposed method can reduce time consumption while improving the image contrast and can get satisfactory results.

Underwater Image Saliency Detection Based on Improved Histogram Equalization

In order to solve the problem of unsatisfactory detection effect of underwater visual saliency map, an image saliency detection algorithm based on improved histogram equalization is proposed. Underwater images are often not clear enough because the refraction of light underwater causes insufficient image resolution. Therefore, in order to solve the existing problems of traditional histogram equalization algorithm, an improved histogram equalization method is proposed to enhance the quality of images, which makes the saliency regions smoother and clearer. In this paper, the simulation experiments were conducted on UIEBD dataset and DLOU_underwater dataset. The experimental results show the effectiveness, robustness and accuracy of the proposed algorithm.

A Fast Underwater Optical Image Segmentation Algorithm Based on a Histogram Weighted Fuzzy C-means Improved by PSO

The S/N of an underwater image is low and has a fuzzy edge.If using traditional methods to process it directly,the result is not satisfying.Though the traditional fuzzy C-means algorithm can sometimes divide the image into object and background,its time-consuming computation is often an obstacle.The mission of the vision system of an autonomous underwater vehicle (AUV) is to rapidly and exactly deal with the information about the object in a complex environment for the AUV to use the obtained result to execute the next task.So,by using the statistical characteristics of the gray image histogram,a fast and effective fuzzy C-means underwater image segmentation algorithm was presented.With the weighted histogram modifying the fuzzy membership,the above algorithm can not only cut down on a large amount of data processing and storage during the computation process compared with the traditional algorithm,so as to speed up the efficiency of the segmentation,but also improve the quality of underwater image segmentation.Finally,particle swarm optimization (PSO) described by the sine function was introduced to the algorithm mentioned above.It made up for the shortcomings that the FCM algorithm can not get the global optimal solution.Thus,on the one hand,it considers the global impact and achieves the local optimal solution,and on the other hand,further greatly increases the computing speed.Experimental results indicate that the novel algorithm can reach a better segmentation quality and the processing time of each image is reduced.They enhance efficiency and satisfy the requirements of a highly effective,real-time AUV.

Enhancement Technique of Image Contrast using New Histogram Transformation

This paper presents a preprocessing technique that can provide the improved quality of image robust to illumination changes. First, in order to enhance the image contrast, we proposed new adaptive histogram transformation combining histogram equalization and histogram specification. Here, by examining the characteristic of histogram distribution shape, we determine the appropriate target distribution. Next, applying the histogram equalization with an image histogram, we have obtained the uniform distribution of pixel values, and then we have again carried out the histogram transformation using an inverse of target distribution function. Finally we have conducted various experiments that can enhance the quality of image by applying our method with various standard images. The experimental results show that the proposed method can achieve moderately good image enhancement results.

An Alternate Approach for Designing a Domain Specific Image Search Prototype Using Histogram

Everyone knows that thousand of words are represented by a single image. As a result, image search has become a very popular mechanism for the Web-searchers. Image search means, the search results are produced by the search engine should be a set of images along with their Web-page Unified Resource Locator (URL). Now Web-searcher can perform two types of image search, they are “Text to Image” and “Image to Image” search. In “Text to Image” search, search query should be a text. Based on the input text data, system will generate a set of images along with their Web-page URL as an output. On the other hand, in “Image to Image” search, search query should be an image and based on this image, system will generate a set of images along with their Web-page URL as an output. According to the current scenarios, “Text to Image” search mechanism always not returns perfect result. It matches the text data and then displays the corresponding images as an output, which is not always perfect. To resolve this problem, Web researchers have introduced the “Image to Image” search mechanism. In this paper, we have also proposed an alternate approach of “Image to Image” search mechanism using Histogram.

Application of Image Enhancement Techniques to Potential Field Data

In this paper the application of image enhancement techniques to potential field data is briefly described and two improved enhancement methods are introduced. One method is derived from the histogram equalization technique and automatically determines the color spectra of geophysical maps. Colors can be properly distributed and visual effects and resolution can be enhanced by the method. The other method is based on the modified Radon transform and gradient calculation and is used to detect and enhance linear features in gravity and magnetic images. The method facilites the detection of line segments in the transform domain. Tests with synthetic images and real data show the methods to be effective in feature enhancement.

Infrared Image Real-time Enhancement Based on DSP

Since image real-time processing requires vast amount of computation and high-speed hardware,it is difficult to be implemented with general microcomputer system. In order to solve the problem,a powerful digital signal processing (DSP) hardware system is proposed,which is able to meet needs of image real-time processing.There are many approaches to enhance infrared image.But only histogram equalization is discussed because it is the most common and effective way.On the basis of histogram equalization principle,the specific procedures implemented in DSP are shown.At last the experimental results are given.

Comparative analysis of different methods for image enhancement

Image enhancement technology plays a very important role to improve image quality in image processing. By enhancing some information and restraining other information selectively, it can improve image visual effect. The objective of this work is to implement the image enhancement to gray scale images using different techniques. After the fundamental methods of image enhancement processing are demonstrated, image enhancement algorithms based on space and frequency domains are systematically investigated and compared. The advantage and defect of the above-mentioned algorithms are analyzed. The algorithms of wavelet based image enhancement are also deduced and generalized. Wavelet transform modulus maxima(WTMM) is a method for detecting the fractal dimension of a signal, it is well used for image enhancement. The image techniques are compared by using the mean(μ),standard deviation(?), mean square error(MSE) and PSNR(peak signal to noise ratio). A group of experimental results demonstrate that the image enhancement algorithm based on wavelet transform is effective for image de-noising and enhancement. Wavelet transform modulus maxima method is one of the best methods for image enhancement.

Enhancement algorithm for underwater weld seam image

It is hard to treat the underwater weld seam images for the reason of bad brightness, low contrast and less welding seam information, so a new enhancement algorithm is proposed here. Firstly, the high frequency component was separated by Gaussian filter from origin image, and then it is processed by improved local contrast enhancement(LCE) algorithm to enhance the edge information. Secondly, the gamma transform with adaptive parameters was used to strengthen the image brightness, furthermore, contrast limited adaptive histogram equalization(CLAHE) algorithm was applied to enhance the image contrast. Finally, the two manipulated images were integrated together to obtain the desired image. Experiments on typical images were carried out, and evaluation results showed that this designed algorithm can effectively improve image contrast, highlight welding seam information. Moreover, the image average grey value was moderate, and the information entropy and average gradient were much higher than other algorithms.

The Method of Flotation Froth Image Segmentation Based on Threshold Level Set

A novel flotation froth image segmentation based on threshold level set method is put forward in view of the problem of over-segmentation and under-segmentation which occurs when the existing method segmented the flotation froth images. Firstly, the proposed method adopts histogram equalization to improve the contrast of the image, and then chooses the upper threshold and lower threshold from grey value of histogram of the image equalization, and complete image segmentation using the level set method. In this paper, the model which integrates edge with region level set model is utilized, and the speed energy term is introduced to segment the target. Experimental results show that the proposed method has better segmentation results and higher segmentation efficiency on the images with under-segmentation and incorrect segmentation, and it is meaningful for ore dressing industrial.

Image Preprocessing Methods Used in Meteorological Measurement of the Temperature Testing System

The meteorological measurement automatic temperature testing system has a defective image. To solve the problem such as noise and insufficient contrast, and put forward the research program for image pretreatment, the median filter, histogram equalization and image binarization, methods were used to remove noise and enhance images. Results showed that feature points were clear and accurate after the experiment. This simulation experiment prepared for the follow-up subsequent recognition process.

基于强噪声条件下的激光图像边缘检测研究

边缘检测是激光图像处理的一个重要步骤,针对当前激光图像边缘检测误差大,易受噪声干扰等缺陷,为了获得完整的激光图像边缘,设计了基于强噪声条件下的激光图像边缘检测方法。首先采集激光图像,采用直方图均衡技术对原始激光图像进行增强处理,改善激光图像质量,然后采用小波变换去除激光图像噪声,并采用Canny算子进行激光图像边缘检测,最后测试结果表明,与当前激光图像边缘检测方法相对比,本方法能够检测出更多激光图像边缘细节,激光图像边缘检测精度的平均值超过93%,而且激光图像边缘检测时间控制60 ms以内,为激光图像提供了一种高精度检测技术。

基于改进直方图均衡的SF6泄漏区域增强算法

SF6气体红外成像易受环境噪声影响,呈低对比度与低信噪比特性。导致现有算法无法自适应增强SF6泄漏区域和抑制高斯噪声。针对上述问题,提出一种基于改进HE的SF6泄漏区域增强算法。该算法首先采用单尺度Retinex处理原始SF6图像获得反射图像,然后利用引导滤波将反射图像分解为细节层和基本层,最后采用改进的直方图均衡化来自适应处理基本层,并将增强后的图像进行融合来获得最终的图像。实验结果表明:本文算法不仅能够自适应增强泄漏区域的对比度,而且具有良好边缘保持特性和抑制高斯噪声的性能。其增强效果优于现有的SF6红外图像增强算法。有效改善了SF6红外图像低对比度和低信噪比特性。

基于CLAHE-PCA的矿井低照度图像增强研究

地下矿山巷道环境往往面临光线不足,难以通过获取其暗通道图像判断岩体剥落等异常情况。针对矿井巷道暗通道图像对比度低的问题,提出了一种基于CLAHE-PCA的图像增强算法。首先使用CLAHE算法将获取的矿井巷道原始暗通道图像做对比度增强处理,然后使用自适应Gamma算法对亮度低的图像予以增加对比度矫正;将矫正后获得的灰度图转为RGB图像,通过PCA对其进行平滑处理,以便更多地还原暗通道图像的细节。以峰值信噪比、结构相似性、平均梯度和信息熵等作为评价指标,对试验结果进行验证。结果表明:该方法能够有效处理低对比度的矿井巷道图像,处理后的图像结构相似性达到93%,鲁棒性强,同时能够更多地还原图像的细节。

用于实时图像增强的灰度等间距密度均衡改进算法

实时图像的增强处理,除了保证单帧图像具有较高的对比度,还应考虑处理算法的实时性和图像序列间增强效果的连贯性。灰度等间距密度均衡算法通过去除冗余灰度级后进行有效灰度级等间距重排,可以有效增强图像对比度。本文以灰度等间距密度均衡增强算法为基础,对其有效灰度级有效阈值选取、过度增强等问题进行了相应改进,以满足连续图像序列的实时增强处理需求。实验结果表明,改进算法可以有效避免图像局部变化对整体增强效果的影响。对于1920×1080分辨率的图像,单帧处理时间约为32 ms。本改进算法对于连续图像序列具有良好的增强效果,且能够较好地满足实时图像增强对于连贯性、实时性、鲁棒性的处理需求。

基于改进凸包缺陷算法的扎带定位方法

针对传统视觉在检测捆扎带时存在难检、漏检、定位扎带位置困难和速度慢等问题,提出一种基于改进凸包缺陷算法的扎带定位方法。采用自适应直方图均衡化图像增强算法,以增强编织袋区域与背景的对比度;利用快速凸包算法获得编织袋轮廓的凸包,减少获取凸包的时间;最后,通过改进凸包缺陷算法对编织袋轮廓凸包进行缺陷检测,根据检测结果中凸包缺陷点的位置和缺陷深度进行筛选得到扎带定位点。同时为验证该算法的准确性与鲁棒性,在具有复杂背景干扰的环境下进行实验,将传统凸包缺陷算法与改进后的凸包缺陷算法进行对比分析。实验结果表明:相比于传统凸包缺陷算法无法检测出编织袋轮廓的全部缺陷从而存在漏检,改进后的凸包缺陷算法漏检率为0,定位误差小于4 mm,可有效定位扎带位置并具有较高的鲁棒性。

线性变换与局部均衡融合的红外图像增强

为了改善红外图像的效果,提升对比度和清晰度,丰富边缘细节信息,提出了融合线性变换和局部均衡的红外图像增强方法。利用图像的像素值分布,对像素值进行自适应的分段线性变换,并用局部的直方图均衡增强图像;分别计算两张增强图像的权重图。对比度权重、显著性权重和亮度分布权重;以拉普拉斯金字塔和高斯金字塔的方式,分别对增强图像和权重图进行分解,将分解的图像与权重图进行多尺度线性融合,获得效果理想的增强图像。结果表明,相对于现有方法,本文中提出的方法增强图像的视觉效果更清晰,信息熵、平均梯度和变异系数分别比现有方法高出9.03%、23.87%和9.97%以上。该研究可更有效地提高红外图像增强的性能。

复杂特征全景图像视觉传达效果优化仿真

由于全景拍摄中不同相机镜头的焦点距离不固定,因此在拼接之前相邻两个局部图像间就会产生视差,且距离越近的物体产生视差会越大。全景图像效果的优化是提升视觉质量的关键。提出一种复杂特征全景图像视觉传达效果优化方法。利用两种色彩空间模型与二维经验小波变换法,提取出图像的光谱特征与纹理特征。采用马拉特算法完成小波变换与小波分解,得到复杂特征融合后的全景图像重构结果。基于能量函数及负相关性,界定视觉传达效果的降噪优化目标,设计四阶偏微分方程的降噪模型,完成图像降噪处理。基于此,融合冲击滤波器与直方图均衡化,根据各连通分量对应的子图像,拉伸图像灰度级分布形式,增强细节信息,实现视觉传达效果优化。选取一幅具有复杂特征的全景图像,从感官与数据量化两方面评价不同方法下全景图像的优化效果。实验结果验证了上述方法提高了图像的对比度、清晰度、丰富度,且处理时间更短,具备良好的实践性与实时性。