Gamma Correction for Brightness Preservation in Natural Images

Due to improper acquisition settings and other noise artifacts,the image degraded to yield poor mean preservation in brightness.The simplest way to improve the preservation is the implementation of histogram equalization.Because of over-enhancement,it failed to preserve the mean brightness and produce the poor quality of the image.This paper proposes a multi-scale decomposi-tion for brightness preservation using gamma correction.After transformation to hue,saturation and intensity(HSI)channel,the 2D-discrete wavelet transform decomposed the intensity component into low and high-pass coefficients.At the next phase,gamma correction is used by auto-tuning the scale value.The scale is the modified constant value used in the logarithmic function.Further,the scale value is optimized to obtain better visual quality in the image.The optimized value is the weighted distribution of standard deviation-mean of low pass coefficients.Finally,the experimental result is estimated in terms of quality assessment measures used as absolute mean brightness error,the measure of information detail,signal to noise ratio and patch-based contrast quality in the image.By comparison,the proposed method proved to be suitably remarkable in retaining the mean brightness and better visual quality of the image.

基于最大图像熵Gamma校正估计的图像特征点检测和匹配方法

提出一种基于最大图像熵Gamma校正估计的图像特征点检测和匹配方法。通过预处理算法来增强图像的对比度,将其应用于图像特征点的检测和匹配。在预处理阶段,首先采用对数函数对图像进行归一化,根据设定的阈值将图像分为明亮和黑暗分量;然后分别对两个分量自适应地选择不同的参数进行Gamma校正,并且确定使其熵最大化的校正参数为每个分量的最佳参数;最后将上述参数应用于Gamma校正生成对应原始图像明亮和黑暗区域的矫正图,并进行融合生成增强的图像。对预处理完的图像进行特征点检测和匹配实验。研究结果表明:所提出的算法相较于未处理算法、HE和自适应Gamma算法,特征点检测后的匹配数量在欠曝光图像上分别提升85.7%、26.4%和15.2%,在过曝光图像上分别提升59.4%、12.2%和103.8%。匹配效果也有较明显提升。

基于间断性Gamma校正的粘连蚕茧分割方法研究

目前,蚕茧分割计数研究主要针对大小几乎完全相同的蚕茧,难以应对大小差异较大的情况。针对上述问题,采用图像增强的方法提取蚕茧表面并进行蚕茧的粘连分割。首先,将原图直接转化为灰度图像进行腐蚀处理,通过Gamma校正使得图像灰度值收缩;其次,通过限制对比度自适应直方图均衡化(CLAHE)方法提高蚕茧图像与其背景区域的灰度差,使其进一步分离;再利用高斯滤波和腐蚀方法平滑并收缩蚕茧图像,根据亮度数据进行第二次Gamma校正以调整对比度;最后,通过二值化处理统计连通域个数得出蚕茧数量。实验结果表明,所提方法针对大小蚕茧分割适应力较强,平均准确率高达99%。

Industrial Equipment Troubleshooting with Imaging Technique Improved Gamma-Ray Absorption Scans

Gamma scanning is one of the most common nuclear techniques on troubleshooting industrial equipments like distillation columns and reactors. With a very simple concept, the technique is easy to implement. Searching for a competitive edge the industry has been long developing solutions to achieve better results. On the last decades, significant development has been done with the advent of new equipments, electronics, portable computers and software. Continuous scanning and wireless detection systems are examples of successful field solutions, while new software aid on reporting and data presentation. However the type and quality of the results itself has not dramatically changed since its beginning. A scan profile is simple to understand, although the process to build it can be very complex as it requires a specific blend of knowledge and abilities. Process engineering, chemical engineering, internal hydraulic project, nuclear engineering and field abilities are pre requisites for of any scan specialist. Correct data gathering, interpretation and reporting are abilities often difficult to match or requires a long time of training. The industry faces a similar difficult on the customer side, as it is always necessary to train end users to understand a report and how to use its best. This paper describes our effort on developing a new approach on the gamma scan test using image reconstruction techniques that would result on a graphic image rather than a XY plot. Direct and easier to understand, a report with graphic images would be also be accessible to a wider audience, not limited to the customers experienced with gamma scan interpretation.

改进Gamma变换的遥感影像阴影补偿方法

影像阴影是遥感影像应用与解译的重要干扰项,为了提高阴影补偿精度,更真实地还原遥感影像上地物特征信息,提出一种基于提取同质区域的改进Gamma变换的阴影补偿方法.选取了两张具有不同地物特征的遥感影像作为实验区,首先对实验区影像进行蓝光抑制;其次,通过膨胀运算和腐蚀运算求取阴影区域与非阴影同质区域,构造实验区所需要的结构元素;最后,针对同质区域通过Gamma变换对遥感影像进行阴影补偿,与Retinex算法和局部补偿算法进行精度比较.实验结果表明,该方法在每个颜色通道的平均梯度都要优于另外两种算法,从而验证了该文方法对提升遥感影像阴影补偿精度的有效性,为提高遥感影像阴影补偿精度提供了一种新思路.

Underwater image enhancement based on red channel weighted compensation and gamma correction model

Due to the special characteristics of light in water,the information of the red channel is seriously attenuated in collected image.This causes other colors to dominate the image.This paper proposes an underwater image enhancement algorithm based on red channel weighted compensation and gamma correction model.Firstly,by analyzing the attenuation characteristics of RGB channels,the intensity and the edge information of red channel are compensated by weighting the attenuation coefficient ratio between different channels to correct the chromaticity.Then the gamma correction model is employed to stretch the intensity range to enhance the contrast which makes the image look clearer.The experimental results show that the proposed algorithm can correct the color cast effect and improve the contrast by nearly 2 times for the underwater images with too much red component attenuation.

多尺度自适应Gamma矫正的低照图像增强

为了在提高低照图像的亮度和对比度的同时,保持图像的色彩自然度,提出了多尺度自适应Gamma矫正的低照图像增强方法.鉴于HSI颜色空间的明度分量Ⅰ决定着图像的明暗程度,以及明度分量的多尺度特性,首先,将图像转换到HSI颜色空间,对明度分量Ⅰ进行多尺度Retinex分解;然后,对分解得到的多尺度光照图像分别进行自适应的Gamma矫正,其中Gamma指数自适应于暗区像素的占比,鲁棒地改善光照图像的光照分布,经Retinex反变换得到增强的多尺度明度分量;最后,将增强的多尺度明度分量的线性融合作为增强的明度分量Ⅰ’,与色相分量H和饱和度分量S重组,得到最后的增强图像.实验结果表明,相对于部分最新提出的现有方法,新方法的PSNR和SSIM值分别高出1.19 dB和1.8%以上,具有更好的增强效果和鲁棒性,增强图像的视觉效果更适宜,对比度更高.

A novel approach for feature extraction from a gamma‑ray energy spectrum based on image descriptor transferring for radionuclide identification

This study proposes a novel feature extraction approach for radionuclide identification to increase the precision of identification of the gamma-ray energy spectrum set.For easier utilization of the information contained in the spectra,the vectors of the gamma-ray energy spectra from Euclidean space,which are fingerprints of the different types of radionuclides,were mapped to matrices in the Banach space.Subsequently,to make the spectra in matrix form easier to apply to image-based deep learning frameworks,the matrices of the gamma-ray energy spectra were mapped to images in the RGB color space.A deep convolutional neural network(DCNN)model was constructed and trained on the ImageNet dataset.The mapped gamma-ray energy spectrum images were applied as inputs to the DCNN model,and the corresponding outputs of the convolution layers and fully connected layers were transferred as descriptors of the images to construct a new classification model for radionuclide identification.The transferred image descriptors consist of global and local features,where the activation vectors of fully connected layers are global features,and activations from convolution layers are local features.A series of comparative experiments between the transferred image descriptors,peak information,features extracted by the histogram of the oriented gradients(HOG),and scale-invariant feature transform(SIFT)using both synthetic and measured data were applied to 11 classical classifiers.The results demonstrate that although the gamma-ray energy spectrum images are completely unfamiliar to the DCNN model and have not been used in the pre-training process,the transferred image descriptors achieved good classification results.The global features have strong semantic information,which achieves an average accuracy of 92.76%and 94.86%on the synthetic dataset and measured dataset,respectively.The results of the statistical comparison of features demonstrate that the proposed approach outperforms the peak-searching-based method,HOG,and SIFT on the synthetic and measured datasets.

High-resolution SPECT for small-animal imaging

This article presents a brief overview of the development of high-resolution SPECT for small-animal im- aging. A pinhole collimator has been used for high-resolution animal SPECT to provide better spatial resolution and detection efficiency in comparison with a parallel-hole collimator. The theory of imaging characteristics of the pin- hole collimator is presented and the designs of the pinhole aperture are discussed. The detector technologies used for the development of small-animal SPECT and the recent advances are presented. The evolving trend of small-animal SPECT is toward a multi-pinhole and a multi-detector system to obtain a high resolution and also a high detection ef- ficiency.

A novel algorithm for transmission image reconstruction of tomographic gamma scaners

It is difficult to develop image reconstruction algorithms for tomographic gamma scanning based on drummed radioactive residues or wastes.In this paper,a novel reconstruction algorithm of transmission image for tomographic gamma scanning is proposed.It is based on the conventional transmission equation and equivalent gamma-ray track length modified by a Monte Carlo method.The algorithm is implemented by simulating the samples on the established platform.For the verification experiments of the algorithm,several cubic voxel samples were designed and manufactured.Experimental tests were conducted.The tomographic gamma scanning of transmission images is compared with the linear attenuation coefficients by the simulated values and experimental data with the algorithm and the reference values.The results show that the absolute relative errors of the reconstructed images are less than 5%.

Difference in electron-and gamma-irradiation effects on output characteristic of color CMOS digital image sensors

Changes of the average brightness and non-uniformity of dark output images,and quality of pictures captured under natural lighting for the color CMOS digital image sensorsirradiated at different electron doses have been studied in comparison to those from theγ-irradiated sensors. For the electron-irradiated sensors, the non-uniformity increases obviouslyand a small bright region on the dark image appears at the dose of 0.4 kGy. The average brightnessincreases at 0.4 kGy, increases sharply at 0.5 kGy. The picture is very blurry only at 0.6 kGy,showing the sensor undergoes severe performance degradation. Electron radiation damage is much moresevere than γ radiation damage for the CMOS image sensors. A possible explanation is presented inthis paper.

Gamma变换与多尺度细节增强的红外图像增强

针对现有的红外图像增强方法存在欠增强、过增强以及微小细节不清晰等缺陷,提出了一种Gamma变换与多尺度细节增强的红外图像增强方法。该方法充分利用红外图像边缘细节的多尺度特性,用多尺度的引导滤波将红外图像分解为多尺度的细节层和基础层;对基础层进行自适应的Gamma变换,Gamma指数自适应于基础层图像亮区像素数与暗区像素数的比值,旨在适宜地调整红外图像的整体亮度,使得隐藏于暗区的图像信息得以正常显示;对多尺度的细节层分别进行四邻域的拉普拉斯增强,以提升多尺度细节信息的信号强度。实验数据证明,相对于部分最新的现有方法,本文方法具有更好的增强效果,增强图像的视觉感知效果更佳,客观的图像质量指标平均梯度和无参考图像质量指标均较高。

基于双Gamma指数的主动声呐图像灰度校正方法

针对主动声呐在工作时受强混响影响,声呐图像中目标亮点显示被严重压缩导致视觉效果差、目标人工筛选困难的问题,提出一种基于双Gamma指数的主动声呐图像灰度校正方法。该方法对传统单Gamma指数图像灰度校正方法进行改进,针对主动声呐图像特点,将像素点根据灰度最大类间方差计算结果分为混响灰度区间、背景灰度区间以及目标灰度区间,对不同区间分别构建Gamma指数函数,实现灰度分区调整。实验数据分析结果表明,该方法相对于单Gamma指数图像灰度校正方法能够更好地适应复杂背景,在改善视觉效果的同时,提高了目标与背景间的对比度。

偏头痛脑代谢物改变及与认知情绪关系的MRS研究

目的探讨利用MRS技术分析偏头痛患者大脑中的神经递质变化及其与认知、情绪改变的关系。方法选取31例偏头痛(MA)患者为MA组,28例健康受试者作为对照组。使用MRS分析前扣带回皮层(ACC)、小脑齿状核(DN)及左侧岛叶(LINS)的γ氨基丁酸(GABA)、谷氨酸(Glu)和谷胱甘肽(GSH)水平的变化,并观察其与认知、情绪及偏头痛特征的相关性。结果与对照组比较,MA组表现出认知损害和焦虑症状;ACC中GSH水平增高(P=0.010),DN中GABA(P=0.043)和LINS中GABA(P=0.029)水平降低;DN中GABA与连线试验呈负相关(r=-0.542,P=0.002),LINS中GABA与MSQ(r=-0.441,P=0.017)、PHQ-9(r=-0.400,P=0.031)呈负相关。结论本文为偏头痛患者认知、情绪变化的分析提供了新的视角。

煤矿井下非均匀照度图像去噪研究

煤矿综采工作面空间小、照明环境复杂多变,采煤过程中伴随着大量的粉尘、大雾,导致采集的图像出现曝光、细节特征减弱等问题,难以对井下照明区域光照强度过大的图像进行有效的特征提取。针对上述问题,提出了一种煤矿井下非均匀照度图像去噪算法。首先,将视频截取为图像,判断图像是否需要进行光照抑制,将需要进行光照抑制的RGB图像拆分通道,并计算每个通道的光照调节因子,实现图像的整体光照调节;然后,将未进行整体光照抑制的图像和经整体光照抑制的图像进行反射分量提取,即将输入的图像转换为HSV空间图像,使用单尺度Retinex(SSR)算法对V通道图像中的光照分量进行单独处理,将V分量中的入射分量去除,保留反射分量,并对反射分量使用直方图均衡算法实现光照均衡化处理;最后,使用基于引导滤波的暗通道先验算法对经过光照处理后的图像进行去雾处理,并使用伽马校正函数重新调节亮度不均的图像。主观评价结果表明:提出的煤矿井下非均匀照度图像去噪算法有效抑制了因光照导致整体亮度较高的问题,且由于大雾、粉尘等因素导致图像模糊的部分更加清晰,图像的细节特征更加突出。采用信息熵、均值、标准差、空间频率4种评价指标对提出的算法效果进行客观评价,结果表明,提出的算法在信息熵、均值、标准差、空间频率上较多尺度Retinex(MSR)算法分别平均提升了21.87%,-56.06%,153.43%,294.45%,较基于颜色保持的多尺度视网膜增强(MSRCP)算法分别平均提升了1.18%,-39.56%,33.29%,-4.71%,较带色彩恢复的多尺度视网膜增强(MSRCR)算法分别平均提升了38.06%,-55.27%,462.10%,300.96%,说明提出的算法能更有效地增加图像信息量、抑制光照强度、提升边缘信息及图像清晰度。

基于正则化约束的方位伽马成像测井分辨率提高方法

随钻方位伽马测井可以用于地层放射性强度分析及地质导向、储层界面识别,但受到统计误差的影响非常大,曲线和成像分辨率不高,特别是在对比度不高的地层中,这种影响更明显。为了提高随钻方位伽马测井的成像分辨率,提出一种基于仪器响应特性对方位伽马数据进行正则化处理的方法,以获得更准确的地层放射性强度。首先根据探测器响应原理,采用数值模拟和实验方法得到方位伽马测井的响应灵敏函数,并验证得到的灵敏因子的准确性。然后,通过构造正则化计算公式、调整计算参数对测量的原始方位伽马数据进行处理。结果表明:经该方法处理后得到方位伽马曲线的聚焦性能、对比度得到提高,避免了过去使用线性求解方法所导致的过拟合问题,且伽马成像分辨率有显著增强,薄层识别能力也得到一定改善。该方法能够提供更加准确和清晰的成像结果,提高仪器的地质导向能力和储层钻遇率。

基于多尺度梯度域引导滤波的煤矿井下图像增强方法

煤矿井下图像存在较严重的光照不均匀和噪声干扰,现有基于Retinex的方法直接应用于煤矿井下图像增强易出现光晕伪影、边缘模糊、过增强和噪声放大等问题。针对上述问题,提出了一种基于多尺度梯度域引导滤波的煤矿井下图像增强方法。首先,将多尺度思想引入梯度域引导滤波中,实现对非均匀光照的准确估计,有效解决了增强图像时光晕伪影及边缘模糊的问题。然后,利用Retinex模型分离出光照分量和反射分量:对于光照分量,通过自适应伽马校正函数逐像素地修正光照信息,实现对图像暗区域增强的同时,抑制亮区域过增强,并使用限制对比度自适应直方图均衡化方法调整图像对比度;对于反射分量,将梯度域引导滤波与多尺度细节提升相结合,在准确去除噪声后提升纹理细节,避免了增强图像时噪声放大的问题。最后,将处理后的光照分量及反射分量融合,计算图像增益系数,并使用线性色彩恢复方法实现对原始RGB图像的逐像素增强,提升方法处理效率。实验结果表明,从主客观角度与现有方法相比,经所提方法处理后的图像在色彩保持、对比度、噪声抑制、细节保留等方面均取得了较好的增强效果,同时处理效率较高。

LEGAN:一种新的暗弱光照图像增强算法

针对暗弱光照图像所存在的亮度、对比度、信噪比低,以及噪声污染大等问题,提出了一种新的暗弱光照图像增强算法LEGAN。该算法将图像输入至所提伽马曲线估计网络求得包含伽马参数的特征图,再经过LEB模块增强亮度,并通过级联LEB的方式迭代增强结果。采用基于PatchGAN的全局-局部判别器结构来提高图像分辨率和恢复图像细节。通过引入感知损失来限制真实标签和输出结果之间的差距,利用照明平滑度损失保持相邻像素之间的单调性关系,同时结合空间一致性损失来增强图像的空间相关性。实验结果表明,相比于现今大多数主流增强算法,该算法的细节还原度相对较高,且能有效避免增强后的图像出现局部亮度不佳等问题。

考虑多尺度纹理特征的红外传感图像频域增强

红外传感图像质量容易受探测器和传输距离影响,导致图像亮度和对比度较低、轮廓细节模糊等问题。为此,提出了考虑多尺度纹理特征的红外传感图像频域增强方法。引入残差学习策略,基于多尺度纹理特征搭建多尺度卷积神经网络模型,进行图像去噪。对去噪后图像进行傅里叶变换,获取红外传感图像的低频图像和高频图像。针对低频图像部分,调节图像灰度和对比度以增强低频分量。针对高频图像部分,利用Log算子和Laplace算子增强图像细节及边缘。加权融合两者处理结果,选取Gamma校正调节对比度,增强高频分量。融合两种增强后图像,实现红外传感图像频域增强。实验结果表明,该方法峰值信噪比高于43,信息熵大于8,边缘强度超过82,对比度熵大于8.1,平均梯度大于8。

抗GABABR脑炎四例

目的抗γ氨基丁酸B型受体(GABABR)脑炎四例,以减少临床误诊误治。方法收集4例曾初诊为其他疾病的抗GABABR脑炎的临床资料,并分析其临床特征及误诊原因。结果4例患者均有癫痫发作及认知障碍,2例(例2、例4)伴发精神症状。4例颅脑MRI均发现单侧或双侧颞叶病变。初步诊断分别为胶质瘤3例和短暂性脑缺血发作1例(例4)。误诊时间为12~68d。结论抗GABABR脑炎的临床特征与其他疾病存在交叉,提高对该疾病的认识、尽早完善特异性抗体检查是明确诊断的关键,从而减少临床误诊误治。