Single-image night haze removal based on color channel transfer and estimation of spatial variation in atmospheric light

The visible-light imaging system used in military equipment is often subjected to severe weather conditions, such as fog, haze, and smoke, under complex lighting conditions at night that significantly degrade the acquired images. Currently available image defogging methods are mostly suitable for environments with natural light in the daytime, but the clarity of images captured under complex lighting conditions and spatial changes in the presence of fog at night is not satisfactory. This study proposes an algorithm to remove night fog from single images based on an analysis of the statistical characteristics of images in scenes involving night fog. Color channel transfer is designed to compensate for the high attenuation channel of foggy images acquired at night. The distribution of transmittance is estimated by the deep convolutional network DehazeNet, and the spatial variation of atmospheric light is estimated in a point-by-point manner according to the maximum reflection prior to recover the clear image. The results of experiments show that the proposed method can compensate for the high attenuation channel of foggy images at night, remove the effect of glow from a multi-color and non-uniform ambient source of light, and improve the adaptability and visual effect of the removal of night fog from images compared with the conventional method.

Influences of Atmospheric Turbulence on Image Resolution of Airborne and Space-Borne Optical Remote Sensing System

A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.

A Feasible Atmospheric Correction Method to TM Image

In this paper, an atmospheric correction method to TM image is presented, which can simulate the atmospheric correction parameters, such as optical depth, sky radiance and path radiance at the time the satellite passes,by using interpolation among local meteorological records, parameterization models and dark pixels. The TM image of the Nanjing area in China was corrected by this method. For analyzing the accuracy of this method, the calculated reflectance, apparent reflectance and ground measured reflectance were compared. NDVI before and after atmospheric cor- rection were also compared. The results show that the method is applicable and efficient in the visible to near infrared band of TM image. In order to improve the accuracy of the method, the infrared spectrum measured data for the two other bands of TM image are required in future field investigations. The method is suitable to many other satellite optical remote sensing images with the same or similar spectral characteristics of TM images.

Research on a novel restoration algorithm of turbulence-degraded images with alternant iterations

A new restoration algorithm based on double loops and alternant iterations is proposed to restore the object image effectively from a few frames of turbulence-degraded images, Based on the double loops, the iterative relations for estimating the turbulent point spread function PSF and object image alternately are derived. The restoration experiments have been made on computers, showing that the proposed algorithm can obtain the optimal estimations of the object and the point spread function, with the feasibility and practicality of the proposed algorithm being convincing.

Spatial-Temporal Characterization of Atmospheric Aerosols via Airborne Spectral Imaging and Growing Hierarchical Self-Organizing Maps

Neural network analysis based on Growing Hierarchical Self-Organizing Map (GHSOM) is used to examine Spatial-Temporal characteristics in Aerosol Optical Depth (AOD), Ångström Exponent (ÅE) and Precipitation Rate (PR) over selected East African sites from 2000 to 2014. The selected sites of study are Nairobi (1°S, 36°E), Mbita (0°S, 34°E), Mau Forest (0.0° - 0.6°S;35.1°E - 35.7°E), Malindi (2°S, 40°E), Mount Kilimanjaro (3°S, 37°E) and Kampala (0°N, 32.1°E). GHSOM analysis reveals a marked spatial variability in AOD and ÅE that is associated to changing PR, urban heat islands, diffusion, direct emission, hygroscopic growth and their scavenging from the atmosphere specific to each site. Furthermore, spatial variability in AOD, ÅE and PR is distinct since each variable corresponds to a unique level of classification. On the other hand, GHSOM algorithm efficiently discriminated by means of clustering between AOD, ÅE and PR during Long and Short rain spells and dry spell over each variable emphasizing their temporal evolution. The utilization of GHSOM therefore confirms the fact that regional aerosol characteristics are highly variable be it spatially or temporally and as well modulated by PR received over each variable.

Recent Advances in Image Dehazing

Images captured in hazy or foggy weather conditions can be seriously degraded by scattering of atmospheric particles,which reduces the contrast,changes the color,and makes the object features difficult to identify by human vision and by some outdoor computer vision systems.Therefore image dehazing is an important issue and has been widely researched in the field of computer vision.The role of image dehazing is to remove the influence of weather factors in order to improve the visual effects of the image and provide benefit to post-processing.This paper reviews the main techniques of image dehazing that have been developed over the past decade.Firstly,we innovatively divide a number of approaches into three categories:image enhancement based methods,image fusion based methods and image restoration based methods.All methods are analyzed and corresponding sub-categories are introduced according to principles and characteristics.Various quality evaluation methods are then described,sorted and discussed in detail.Finally,research progress is summarized and future research directions are suggested.

Ghost Imaging Lidar via Sparsity Constraints in Real Atmosphere

We present a series of results acquired at a 2-kilometer distance using our lidar system under several weather conditions, clear, cloudy, light rain, moderately foggy, and night. The experimental results show that ghost imaging lidar via spar-sity constraints can realize imaging in all these weather conditions.

Analysis and Evaluation of the Global Aerosol Optical Properties Simulated by an Online Aerosol-coupled Non-hydrostatic Icosahedral Atmospheric Model

Aerosol optical properties are simulated using the Spectral Radiation Transport Model I~）r Aerosol Species （SPRINTARS） coupled with the Non-hydrostatic ICosahedral Atmospheric Model （NICAM）. The 3-year global mean all-sky aerosol optical thickness （AOT） at 550 nm, theAngstr/Sm Exponent （AE） based on AOTs at 440 and 870 nm, and the single scattering albedo （SSA） at 550 nm are estimated at 0.123, 0.657 and 0.944, respectively. For each aerosol species, the mean AOT is within the range of the AeroCom models. Both the modeled all-sky and clear-sky results are compared with observations from the Moderate Resolution Imaging Spectroradiometer （MODIS） and the Aerosol Robotic Network （AERONET）. The simulated spatiotemporal distributions of all-sky AOTs can generally reproduce the MODIS retrievals, and the correlation and model skill can be slightly improved using the clear-sky results over most land regions. The differences between clear-sky and all-sky AOTs are larger over polluted regions. Compared with observations from AERONET, the modeled and observed all-sky AOTs and AEs are generally in reasonable agreement, whereas the SSA variation is not well captured. Although the spatiotemporal distributions of all-sky and clear-sky results are similar, the clear-sky results are generally better correlated with the observations. The clear-sky AOT and SSA are generally lower than the all-sky results, especially in those regions where the aerosol chemical composition is contributed to mostly by sulfate aerosol. The modeled clear-sky AE is larger than the all-sky AE over those regions dominated by hydrophilic aerosol, while the＇opposite is found over regions dominated by hydrophobic aerosol.

A single image dehazing method based on decomposition strategy

Outdoor haze has adverse impact on outdoor image quality,including contrast loss and poor visibility.In this paper,a novel dehazing algorithm based on the decomposition strategy is proposed.It combines the advantages of the two-dimensional variational mode decomposition(2DVMD)algorithm and dark channel prior.The original hazy image is adaptively decom-posed into low-frequency and high-frequency images according to the image frequency band by using the 2DVMD algorithm.The low-frequency image is dehazed by using the improved dark channel prior,and then fused with the high-frequency image.Furthermore,we optimize the atmospheric light and transmit-tance estimation method to obtain a defogging effect with richer details and stronger contrast.The proposed algorithm is com-pared with the existing advanced algorithms.Experiment results show that the proposed algorithm has better performance in comparison with the state-of-the-art algorithms.

Atmospheric Convection Model Based Digital Confidentiality Scheme

Nonlinear dynamics is a fascinating area that is intensely affecting a wide range of different disciplines of science and technology globally.The combination of different innovative topics of information security and high-speed computing has added new visions into the behavior of complex nonlinear dynamical systems which uncovered amazing results even in the least difficult nonlinearmodels.The generation of complex actions froma very simple dynamical method has a strong relation with information security.The protection of digital content is one of the inescapable concerns of the digitally advanced world.Today,information plays an important role in everyday life and affects the surroundings rapidly.These digital contents consist of text,images,audio,and videos,respectively.Due to the vast usage of digital images in the number of social and web applications,its security is one of the biggest issues.In this work,we have offered an innovative image encryption technique based on present criteria of confusion and diffusion.The designed scheme comprises two major nonlinear dynamical systems.We have employed discrete fractional chaotic iterative maps to add confusion capability in our suggested algorithm and continuous chaotic Lorenz system.We have verified our offered scheme by using statistical analysis.The investigations under the statistical tests suggested that our proposed technique is quite reasonable for the security of digital data.

WLS filter for reducing atmospheric effects in spaceborne SAR tomography

A new approach was presented to eliminate the atmosphere-induced phase error utilizing only the single look complex(SLC) synthetic aperture radar(SAR) image set. This method exploited the space-invariance characteristic of phase error components contained in image pixels and estimates the phase error using the weighted least-squares(WLS) filter. Actually, this sort of method can be classified as autofocus algorithm which was generally applied in airborne SAR 2-D imaging to compensate the phase error introduced by airplane's nonideal motion. Real data processing, which is relevant to Honda center and Angel stadium of Anaheim test-sites and acquired by Envisat-ASAR during the period from June 2004 to October 2007, was carried out to evaluate this WLS estimation algorithm. Experimental results show that the phase error estimated from WLS filter is very accurate and the focusing quality along NSR dimension is improved prominently via phase correction, which verifies the practicability of this new method.

Characteristic of Atmospheric Pressure Plasma Jet in Coplanar Dielectric Barrier Discharge

Non-thermal plasma jet at atmospheric pressure has recently attracted lots of attention because of its applications in plasma bullet or plasma plume.Thus,we studied on generating plasma jet by coplanar dielectric barrier discharge in a device driven by sinusoidal voltage.The processes of plasma discharges in both positive and negative half cycles were recorded using a high-speed ICCD(intensified charge-coupled device)camera;based on the results we estimated the velocity of plasma propagation,and investigated the influence of gas flow on the plasma development.It is shown that the plasma bullets,which have velocity in the order of 103～104m/s,exist only outside the cathode.APPJ(atmospheric pressure plasma jet)is created by the electron beam from the cathode,and then sustained by a strong radial electric field near and outside the cathode.The gas flow influences the APPJ length in air but not the APPJ discharge,while the discharge is affected significantly by the applied voltage.

大气湍流对高分辨率遥感卫星的成像影响研究

遥感卫星在国防和民用探测等领域发挥着重要作用,而大气湍流严重影响高分辨率遥感卫星的成像质量。本文重点研究了遥感卫星对地探测时,相机口径、卫星轨高和大气湍流强度对空间相机成像质量的影响。首先,基于球面波传输模型和Kolmogorov湍流理论,针对空对地探测湍流波前进行仿真。然后,分析畸变波前随相机口径、卫星轨高和大气相干长度的变化规律,并推导出普适公式。在此基础上,进一步推导出空间相机成像分辨率随相机口径、卫星轨高和大气相干长度变化的计算公式。最后,研究了大气湍流对空间相机调制传递函数(MTF)的影响,并以MTF=0.15为基准,仿真分析了MTF相对误差随相机口径、卫星轨高和大气相干长度的变化规律。本研究为高分辨率遥感卫星的设计、分析和评估提供理论依据。

结合光吸收和大气散射模型的可解释图像去雾网络

在雾霾天气下拍摄的图像存在对比度低和颜色偏移的问题,这会进一步影响后续高级计算机视觉应用的准确性。大气散射模型(Atmospheric Scattering Model,ASM)作为图像去雾中的热门技术之一,越来越受到研究者的关注。然而,现有的基于ASM的方法由于忽略了光陷现象而会引起暗淡效应。为了解决这个问题,构建了一种基于光吸收的可解释图像去雾网络。具体地,首先构建了一种基于光吸收的大气散射模型(Light Absorption-based ASM,LA-ASM),在ASM中引入了由纹理密度和场景深度定义的光吸收系数。此外,构建了一种可解释网络LA-ASMNet,该网络通过学习LA-ASM的雾气成分来辅助图像去雾。基于RESIDE数据集的仿真结果证明了LA-ASMNet在缓解图像去雾暗淡效应方面的有效性。

面向大气湍流畸变场景的卷积图像校正方法

大气湍流引发的折射率在时间和空间上的随机波动,会使远程成像系统捕获的图像出现时空模糊和几何畸变,严重削弱了图像的视觉效果和应用价值。针对这一问题,许多学者尝试采用多帧幸运区的方法和基于卷积神经网络的深度学习方法来修复大气湍流引起的图像畸变。然而,在强湍流情况下,这些方法通常模型训练难度较大,湍流自适应性校正能力较为薄弱。为了解决上述问题,经研究提出了一种改进的深度学习网络来提升湍流畸变校正的性能。该网络采用Transformer端到端的网络结构,利用多头自注意力跨通道捕获局部上下文信息;对一级校正网络采用蒙特卡洛Dropout策略进行训练,通过模型不确定性来提取传统方法难以捕获的退化区域;利用提取到的不确定性映射作为引导信息,输入第二级校正网络提升校正准确性。在基于时空模糊加几何畸变的合成湍流退化图像集上进行了实验,证明了提出方法的有效性。

地面目标的中波红外高光谱成像特性测量

利用Telops公司设计的Hyper-Cam中波高光谱成像仪对水泥地上某坦克模型进行3~5μm波段的红外高光谱成像测量。对实验数据进行了逐像素点的大气校正。基于矫正后的实验数据,分析了仪器误差、随机误差、大气校正传递误差等对测试不确定度的影响。分析了该坦克模型在不同谱段上的亮度分布和模型上不同部位的光谱分布特性。结果表明:在波数为2000~3000 cm-1波段,其相对不确定度一直稳定在10%以内,但是在大于3000 cm-1的波段,其误差迅速上升,主要因为常温物体的中波辐射低,在该波段目标辐射与大气路程辐射接近,而引起的噪声增大,造成的测试信噪比降低。该坦克模型光谱辐射亮度测试数据的平均相对不确定度在20%以内,整体误差较低。光谱分布上,坦克各部分特征点和水泥地表的光谱辐射亮度在波长为4.2~5μm波段时的差异性比波长为3.0~4.2μm波段时的差异性更大,且探测器接收到的4.2~5μm波段的光谱辐射亮度要大于3.0~4.2μm的光谱辐射亮度。由于4.3μm波段处于大气吸收带,大气透射率几乎为0,以至于无法获得目标的光谱辐射亮度的真实测量值。空间分布上,模型的辐射亮度主要集中在侧边涂层上,炮口处和履带处的辐射亮度较小。目标各部位辐射亮度分布与周边水泥地表的辐射亮度有着明显差异性。在3~5μm的辐射亮度中,4.4~4.8μm波段的辐射亮度占比较高,该波段成像效果最好。3.2~3.8μm波段的辐射亮度占比较低,该波段成像效果较差。处于大气吸收带的4.2~4.4μm波段,由于大气透射率几乎为0,无法获得其准确的空间辐射亮度分布。研究表明该拓展中波高光谱成像仪在地面目标的中波红外高光谱成像特性研究上具有目标区分精确、误差小、“图像光谱合一”的特点,测试数据可以应用于目标的红外高光谱隐身设计、目标的分类与识别等领域。

非制冷红外热成像测温关键技术研究

非制冷红外热成像测温过程受环境温度、测温距离和大气湿度等诸多因素影响,因此在复杂环境中实现高精度测温颇具挑战。为了满足复杂环境中精确测温的需求,分析并研究了非制冷红外热成像测温误差的主要影响因素和关键补偿技术。首先,针对非制冷红外探测器输出辐射温度易受环境影响的问题,设计了基于粒子群算法优化反向传播神经网络的非制冷红外探测器辐射温度预测算法,实现了不同工作温度下辐射温度的精确预测;其次,针对测温过程中的红外辐射大气衰减现象,设计了基于大气传输软件的近地红外辐射大气透射率计算方法,实现了大气透射率的准确、快速、便捷计算;最后,整合关键误差补偿技术形成了完整的非制冷红外热成像测温方法,并实验验证了以上关键技术对于提高红外测温精度和环境适应性的有效性。

结合自适应融合和双边界光幕估计的图像去雾

针对图像去雾算法存在的细节丢失、颜色失真、去雾不彻底等问题,提出一种利用通道估计光幕上下边界、自适应融合大气光的去雾算法。首先,根据有雾图像最小通道估计大气光幕上边界,以雾图B通道为主、RG通道差补偿结合线性变换得到大气光幕下边界,对上下边界进行逼近优化,经过小波变换融合两边界;其次,分别用RGB三通道的平均值和亮度中通道获取不同的粗糙大气光,以亮度均值为调节因子自适应融合;最后,结合去雾模型复原出无雾图像。实验结果表明,所提算法复原的图像去雾彻底、清晰自然、能保留较多图像细节,具有良好的有效性和鲁棒性。

基于通道剪枝的轻量化空气质量检测方法

针对传统空气质量检测系统结构复杂、部署困难以及成本较高的问题,利用图卷积网络对大气图像特征进行分析,提出了一种基于通道剪枝的轻量化空气质量检测算法。首先以ResNet50为基础网络训练一个PM 2.5指数检测网络,实现了空气质量初步的自动化检测。然后对网络模型中的所有卷积核通道和相关的参数传递进行图节点核权重边建模,以图表示形式输入GCN,并输出针对每个卷积核节点的剪枝重要性判别预测。最后根据GCN结果进行通道剪枝,使用原始数据集对剪枝后模型的参数进行微调,在保持网络检测精准度的情况下,实现网络模型的轻量化。通过对比实验和消融实验验证了提出的检测方法具有较高的检测精度,平均检测误差仅有5.31%,RMSE提升了0.52,R-square仅降低了0.018,解决了网络模型的参数量和计算量过大的问题,网络参数量从4.12×10^(7)降低至2.01×10^(7),FPS从16.78帧/s提升至30.9帧/s,为在便携式终端上实现空气质量检测任务提供了有力的技术支持。