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 overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement al...To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.展开更多
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. T...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.展开更多
Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After makin...Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After making stationary wavelet transform to an infrared image,denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity.For the approximation coefficient matrix with low noisy intensity,enhancement is done by the proposed method based on histogram.The enhanced image can be got by wavelet coefficient reconstruction.Furthermore,an evaluation criterion of enhancement performance is introduced.The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively.At the same time,its amount of calculation is small and operation speed is fast.展开更多
The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circ...The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.展开更多
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.
基金supported by the National Natural Science Foundation of China(61472324)
文摘To overcome the shortcomings of the Lee image enhancement algorithm and its improvement based on the logarithmic image processing(LIP) model, this paper proposes what we believe to be an effective image enhancement algorithm. This algorithm introduces fuzzy entropy, makes full use of neighborhood information, fuzzy information and human visual characteristics.To enhance an image, this paper first carries out the reasonable fuzzy-3 partition of its histogram into the dark region, intermediate region and bright region. It then extracts the statistical characteristics of the three regions and adaptively selects the parameter αaccording to the statistical characteristics of the image’s gray-scale values. It also adds a useful nonlinear transform, thus increasing the ubiquity of the algorithm. Finally, the causes for the gray-scale value overcorrection that occurs in the traditional image enhancement algorithms are analyzed and their solutions are proposed.The simulation results show that our image enhancement algorithm can effectively suppress the noise of an image, enhance its contrast and visual effect, sharpen its edge and adjust its dynamic range.
基金Projects(61376076,61274026,61377024)supported by the National Natural Science Foundation of ChinaProjects(12C0108,13C321)supported by the Scientific Research Fund of Hunan Provincial Education Department,ChinaProjects(2013FJ2011,2014FJ2017,2013FJ4232)supported by the Science and Technology Plan Foundation of Hunan Province,China
文摘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.
基金the Aeronautics Science Foundation of China(20070153005)Astronautics Science Technology Innovation Foundation of China(05C53005)
文摘Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After making stationary wavelet transform to an infrared image,denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity.For the approximation coefficient matrix with low noisy intensity,enhancement is done by the proposed method based on histogram.The enhanced image can be got by wavelet coefficient reconstruction.Furthermore,an evaluation criterion of enhancement performance is introduced.The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively.At the same time,its amount of calculation is small and operation speed is fast.
基金Sponsored by The National Natural Science Foundation of China(60872065)Science and Technology on Electro-optic Control Laboratory and Aviation Science Foundation(20105152026)State Key Laboratory Open Fund of Novel Software Technology,Nanjing University(KFKT2010B17)
文摘The image segmentation difficulties of small objects which are much smaller than their background often occur in target detection and recognition. The existing threshold segmentation methods almost fail under the circumstances. Thus, a threshold selection method is proposed on the basis of area difference between background and object and intra-class variance. The threshold selection formulae based on one-dimensional (1-D) histogram, two-dimensional (2-D) histogram vertical segmentation and 2-D histogram oblique segmentation are given. A fast recursive algorithm of threshold selection in 2-D histogram oblique segmentation is derived. The segmented images and processing time of the proposed method are given in experiments. It is compared with some fast algorithms, such as Otsu, maximum entropy and Fisher threshold selection methods. The experimental results show that the proposed method can effectively segment the small object images and has better anti-noise property.
文摘目的 探讨基于动态对比增强磁共振成像(dynamic contrast enhancement magnetic resonance imaging,DCE-MRI)药代动力学参数直方图特征预测前列腺癌(prostate cancer,PCa)内分泌治疗反应的价值。材料与方法 回顾性分析2018年1月至2023年10月河西学院附属张掖人民医院(中心1)和2020年2月至2023年2月甘肃省人民医院(中心2)PCa患者在内分泌治疗前2周的临床、影像资料,将中心1收集的105例病例按7∶3的比例分为训练集(73例)和内部验证集(32例),将中心2收集的47例病例作为外部验证集。选取DCE-MRI原始图像,通过Siemens Syngo.via工作站获得药代动力学参数容积转运常数(volume transfer contrast,Ktrans)、速率常数(rate contrast,Kep)、血管外细胞外容积分数(extravascular extracellular volume fraction,Ve)伪彩图。在3D Slicer软件中参照轴位T2WI在药代动力学参数伪彩图上逐层勾画全前列腺腺体感兴趣区(region of interest,ROI)后提取直方图特征,经最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)降维筛选出8个最优特征并计算直方图特征。采用单因素及后向多因素logistic回归分析内分泌治疗反应良好组和不良组的独立预测因素,并构建临床模型、直方图特征模型、联合模型。采用受试者工作特性曲线、校准曲线和决策曲线评价模型的效能,通过DeLong检验评估各模型曲线下面积(area under the curve,AUC),最后基于联合模型的独立预测因素绘制列线图。结果 训练集、内部验证集和外部验证集中治疗反应良好组和不良组之间Gleason评分、MRI-T分期、直方图特征差异均存在统计学意义(P<0.001)。后向多因素logistic回归分析显示Gleason评分(OR=0.925,95%CI:0.859~0.958,P=0.038)、MRI-T分期(OR=0.871,95%CI:0.800~0.949,P=0.002)及直方图特征(OR=0.096,95%CI:0.056~0.137,P<0.001)是PCa内分泌治疗反应的独立预测因素;临床模型在训练集、内部验证集及外部验证集的AUC分别为0.857(95%CI:0.774~0.939)、0.953(95%CI:0.888~0.996)、0.808(95%CI:0.676~0.941);直方图特征模型在训练集、内部验证集及外部验证集的AUC为0.874(95%CI:0.769~0.951)、0.816(95%CI:0.664~0.967)、0.674(95%CI:0.517~0.831);联合模型在训练集、内部验证集及外部验证集的AUC为0.951(95%CI:0.906~0.994)、0.973(95%CI:0.922~0.995)、0.830(95%CI:0.699~0.960);决策曲线和校准曲线分析表明,联合模型具有良好的临床应用价值和稳定性;DeLong检验及NRI值显示联合模型的预测效能优于临床模型和直方图特征模型。结论 DCE-MRI药代动力学参数直方图特征是预测PCa内分泌治疗反应的独立预测因素,联合模型在预测PCa内分泌治疗反应方面具有较好的价值,为临床治疗决策提供了新的思路。
