Eigenstructure-based coherence attributes are efficient and mature techniques for large-scale fracture detection. However, in horizontally bedded and continuous strata, buried fractures in high grayscale value zones a...Eigenstructure-based coherence attributes are efficient and mature techniques for large-scale fracture detection. However, in horizontally bedded and continuous strata, buried fractures in high grayscale value zones are difficult to detect. Furthermore, middleand small-scale fractures in fractured zones where migration image energies are usually not concentrated perfectly are also hard to detect because of the fuzzy, clouded shadows owing to low grayscale values. A new fracture enhancement method combined with histogram equalization is proposed to solve these problems. With this method, the contrast between discontinuities and background in coherence images is increased, linear structures are highlighted by stepwise adjustment of the threshold of the coherence image, and fractures are detected at different scales. Application of the method shows that it can also improve fracture cognition and accuracy.展开更多
A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to proc...A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to process poor quality images.展开更多
Histogram equalization is a traditional algorithm improving the image contrast,but it comes at the cost of mean brightness shift and details loss.In order to solve these problems,a novel approach to processing foregro...Histogram equalization is a traditional algorithm improving the image contrast,but it comes at the cost of mean brightness shift and details loss.In order to solve these problems,a novel approach to processing foreground pixels and background pixels independently is proposed and investigated.Since details are mainly contained in the foreground,the weighted coupling of histogram equalization and Laplace transform were adopted to balance contrast enhancement and details preservation.The weighting factors of image foreground and background were determined by the amount of their respective information.The proposed method was conducted to images acquired from CVG⁃UGR and US⁃SIPI image databases and then compared with other methods such as clipping histogram spikes,histogram addition,and non⁃linear transformation to verify its validity.Results show that the proposed algorithm can effectively enhance the contrast without introducing distortions,and preserve the mean brightness and details well at the same time.展开更多
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 ...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.展开更多
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...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.展开更多
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 tw...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.展开更多
针对ORB(oriented FAST and rotated BRIEF)算法中存在匹配精确率低的问题,提出了一种基于LK(Lucas-Kanade)光流改进的ORB图像匹配方法。首先对待处理的图像进行直方图均衡化,然后在Oriented FAST特征点检测的同时用LK光流对其进行跟踪...针对ORB(oriented FAST and rotated BRIEF)算法中存在匹配精确率低的问题,提出了一种基于LK(Lucas-Kanade)光流改进的ORB图像匹配方法。首先对待处理的图像进行直方图均衡化,然后在Oriented FAST特征点检测的同时用LK光流对其进行跟踪,并将跟踪的特征点进行Rotated BRIEF描述,最后在特征匹配筛选环节利用RANSAC(Random Sampling Consistency)算法进行误匹配的剔除。实验结果表明,改进算法在公开数据集中的平均匹配精度为90.9%,平均特征匹配及误匹配的剔除共耗时为18ms,与原始ORB算法相比,在时间基本一致的前提下,有效的提高了匹配的精度。展开更多
The traditional grayscale histogram of an input image is constructed by simply counting its pixels.Hence,the classical his-togram equalization(HE)technique has fundamental defects such as overenhancement,underenhancem...The traditional grayscale histogram of an input image is constructed by simply counting its pixels.Hence,the classical his-togram equalization(HE)technique has fundamental defects such as overenhancement,underenhancement,and brightness drifting.This paper proposes an advanced HE based on a hybrid saliency map and a novel visual prior to addressing the defects mentioned above.First,the texture saliency map and attention weight map are constructed based on the texture saliency and visual attention mechanism.Later,the hybrid saliency map that is obtained by fusing the texture and attention weight maps is used to derive the saliency histogram.Then,a novel visual prior,the narrow dynamic range prior(NDP),is proposed,and the saliency histogram is modified by calculating the optimal parameter in combination with a binary optimization model.Next,the cumulative distribution function(CDF)is rectified to control the brightness.Finally,the hybrid saliency map is applied again for local enhancement.Compared with several state-of-the-art algorithms qualitatively and quantitatively,the proposed algorithm effectively improves the contrast of the image,generates better sub-jective visual perception,and presents better performance broadly.展开更多
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 transforma...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.展开更多
Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,gr...Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color model.The proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images.The proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images.Compared to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram.The proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.展开更多
基金sponsored by the National Science&Technology Major Special Project(Grant No.2011ZX05025-001-04)
文摘Eigenstructure-based coherence attributes are efficient and mature techniques for large-scale fracture detection. However, in horizontally bedded and continuous strata, buried fractures in high grayscale value zones are difficult to detect. Furthermore, middleand small-scale fractures in fractured zones where migration image energies are usually not concentrated perfectly are also hard to detect because of the fuzzy, clouded shadows owing to low grayscale values. A new fracture enhancement method combined with histogram equalization is proposed to solve these problems. With this method, the contrast between discontinuities and background in coherence images is increased, linear structures are highlighted by stepwise adjustment of the threshold of the coherence image, and fractures are detected at different scales. Application of the method shows that it can also improve fracture cognition and accuracy.
文摘A new improved algorithm of histogram equalization was discussed and actualized by analyzing the traditional algorithm. This improved algorithm has better effect than the traditional one, especially it is used to process poor quality images.
基金Sponsored by the National Key R&D Program of China(Grant No.2018YFB1308700)the Research and Development Project of Key Core Technology and Common Technology in Shanxi Province(Grant Nos.2020XXX001,2020XXX009)。
文摘Histogram equalization is a traditional algorithm improving the image contrast,but it comes at the cost of mean brightness shift and details loss.In order to solve these problems,a novel approach to processing foreground pixels and background pixels independently is proposed and investigated.Since details are mainly contained in the foreground,the weighted coupling of histogram equalization and Laplace transform were adopted to balance contrast enhancement and details preservation.The weighting factors of image foreground and background were determined by the amount of their respective information.The proposed method was conducted to images acquired from CVG⁃UGR and US⁃SIPI image databases and then compared with other methods such as clipping histogram spikes,histogram addition,and non⁃linear transformation to verify its validity.Results show that the proposed algorithm can effectively enhance the contrast without introducing distortions,and preserve the mean brightness and details well at the same time.
文摘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.
文摘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.
文摘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.
文摘针对ORB(oriented FAST and rotated BRIEF)算法中存在匹配精确率低的问题,提出了一种基于LK(Lucas-Kanade)光流改进的ORB图像匹配方法。首先对待处理的图像进行直方图均衡化,然后在Oriented FAST特征点检测的同时用LK光流对其进行跟踪,并将跟踪的特征点进行Rotated BRIEF描述,最后在特征匹配筛选环节利用RANSAC(Random Sampling Consistency)算法进行误匹配的剔除。实验结果表明,改进算法在公开数据集中的平均匹配精度为90.9%,平均特征匹配及误匹配的剔除共耗时为18ms,与原始ORB算法相比,在时间基本一致的前提下,有效的提高了匹配的精度。
文摘The traditional grayscale histogram of an input image is constructed by simply counting its pixels.Hence,the classical his-togram equalization(HE)technique has fundamental defects such as overenhancement,underenhancement,and brightness drifting.This paper proposes an advanced HE based on a hybrid saliency map and a novel visual prior to addressing the defects mentioned above.First,the texture saliency map and attention weight map are constructed based on the texture saliency and visual attention mechanism.Later,the hybrid saliency map that is obtained by fusing the texture and attention weight maps is used to derive the saliency histogram.Then,a novel visual prior,the narrow dynamic range prior(NDP),is proposed,and the saliency histogram is modified by calculating the optimal parameter in combination with a binary optimization model.Next,the cumulative distribution function(CDF)is rectified to control the brightness.Finally,the hybrid saliency map is applied again for local enhancement.Compared with several state-of-the-art algorithms qualitatively and quantitatively,the proposed algorithm effectively improves the contrast of the image,generates better sub-jective visual perception,and presents better performance broadly.
文摘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.
基金supported in part by the National Natural Science Foundation of China under Grant No.61662039in part by the Jiangxi Key Natural Science Foundation under No.20192ACBL20031+1 种基金in part by the Startup Foundation for Introducing Talent of Nanjing University of Information Science and Technology(NUIST)under Grant No.2019r070in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)Fund.
文摘Recent contrast enhancement(CE)methods,with a few exceptions,predominantly focus on enhancing gray-scale images.This paper proposes a bi-histogram shifting contrast enhancement for color images based on the RGB(red,green,and blue)color model.The proposed method selects the two highest bins and two lowest bins from the image histogram,performs an equalized number of bidirectional histogram shifting repetitions on each RGB channel while embedding secret data into marked images.The proposed method simultaneously performs both right histogram shifting(RHS)and left histogram shifting(LHS)in each histogram shifting repetition to embed and split the highest bins while combining the lowest bins with their neighbors to achieve histogram equalization(HE).The least maximum number of histograms shifting repetitions among the three RGB channels is used as the default number of histograms shifting repetitions performed to enhance original images.Compared to an existing contrast enhancement method for color images and evaluated with PSNR,SSIM,RCE,and RMBE quality assessment metrics,the experimental results show that the proposed method's enhanced images are visually and qualitatively superior with a more evenly distributed histogram.The proposed method achieves higher embedding capacities and embedding rates in all images,with an average increase in embedding capacity of 52.1%.
