CPU-GPU协同计算的遥感仿真图像MTF退化并行算法

在遥感图像仿真中,为了定量模拟并分析平台抖动、探测器电子特性、大气衰减等因素对遥感成像质量的影响,需要有效计算遥感系统的调制传递函数MTF,并将其快速作用到仿真图像上。然而,由于遥感仿真图像的大数据量特性以及MTF退化包含多个计算密集型算法,使得计算效率成为一个瓶颈问题。为此,根据已有研究提出的MTF计算模型,分析了遥感仿真图像MTF退化的一般流程及主要环节的算法复杂度。在此基础上,提出了一种CPU-GPU协同计算的遥感仿真图像MTF退化并行算法。实验结果表明,该并行算法有效地发挥了GPU并行计算能力,并明显提高了MTF退化处理效率。     

基于CNN的湍流图像退化强度分类研究

湍流图像的复原一直是退化图像领域的研究热点,但依据湍流干扰强度对图像进行分类研究相对较少.不同场景的高空航拍图像进行大气湍流处理.调整湍流退化强度值,生成2000张对应的湍流干扰图像,再对这些图像进行预处理后送入卷积神经网络中进行湍流退化强度分类,最后通过优化搭建的卷积神经网络模型的激活函数以及对学习率的调整进一步提升分类准确率.实验表明,卷积神经网络对不同干扰强度的湍流退化图像分类准确率达到80%左右,结果表明该方法对大气湍流退化图像的复原具有一定指导意义.     

基于贝叶斯理论的湍流退化图像复原方法研究

大气湍流退化图像的复原在航天成像、天文观测等领域具有重要的地位,也是目前亟需解决的问题。该问题的解决能够克服大气湍流扰动带来的图像降质和提高目标图像的分辨能力,以便于后续的目标特征提取和识别等处理。为了对湍流退化图像进行有效复原,提出了一种基于贝叶斯理论的单帧双重循环盲目去卷积图像复原算法,并对该算法的快速实现进行了研究,最后还进行了稳健性分析与测试。实验结果表明,该算法具有较强的稳定性和抗噪声能力,对于缺乏先验知识的情况尤为适用,可见该算法具有实用价值。     

基于二阶加权差分的湍流退化图像快速复原

针对航天飞行器成像系统大气湍流扰动条件下退化图像的快速复原需求,对二维卷积理论进行了探讨,构造了移位算子。提出了基于二维卷积理论和移位算子的复杂背景图像大气湍流退化图像的相邻帧快速复原算法,该算法主要将基于二阶的加权差分极小作为空间相关性约束应用在湍流退化图像的复原校正过程中,自定义几个二阶加权差分项,推导了二阶加权差分算子矩阵快速构造的理论方法。在此基础上,将点扩展函数的非线性估计转化为基于递归迭代的线性方程快速求解,为大气湍流干扰所引起的退化图像的复原校正提供了一种快速的有效方法。实际场景大气湍流退化图像的复原校正实验结果表明,该方法对湍流退化图像的复原效果好,且算法稳定,速度较快。     

基于生成逆推的大气湍流退化图像复原方法

大气湍流是影响远距离成像质量的重要因素。虽然已有的深度学习模型能够较好地抑制大气湍流引起的图像像素几何位移与空间模糊,但是这些模型需要大量的参数和计算量。为了解决该问题,提出了一种轻量化的基于生成逆推的大气湍流退化图像复原模型,该模型包含了去模糊、去偏移和湍流再生成三个核心模块。其中,去模糊模块通过高维特征映射块、细节特征抽取块和特征补充块,抑制湍流引起的图像模糊;去偏移模块通过两层卷积,补偿湍流引起的像素位移;湍流再生成模块通过卷积等操作再次生成湍流退化图像。在去模糊模块中,设计了基于注意力的特征补充模块,该模块融合了通道注意力机制与空间混合注意力机制,能在训练过程中聚焦关注图像中的重要细节信息。在公开的Heat Chamber与自建的Helen两个数据集上,所提模型分别取得了19.94 dB、23.51 dB的峰值信噪比和0.688 2、0.752 1的结构相似性。在达到当前最佳SOTA方法性能的同时,参数量与计算量分别减少了20倍与1.8倍。实验结果表明,该方法对大气湍流退化图像复原有良好的效果。     

基于二次二维经验模态分解去噪的湍流退化图像复原算法

为实现大气湍流环境下的高质量成像,将自适应光学波前探测技术与数字图像处理技术相结合,并提出了一种基于二次二维经验模态分解去噪的湍流退化图像复原算法。通过在光学系统中使用哈特曼—夏克波前传感器探测波前信息,进而计算光学系统点扩散函数;然后使用改进的二次二维经验模态分解算法进行图像去噪,最后利用R-L算法实现对湍流退化图像的复原。通过搭建光学实验系统,对实际拍摄的湍流退化图像进行了复原实验。结果表明,该算法能够有效减弱噪声放大现象,得到更加稳定的高质量大气湍流退化图像复原结果。     

一种基于带雾模糊图像的退化模型

大雾天气条件下获取图像时,由于大气的吸收和散射以及在成像过程中系统发生振颤等,会得到退化的带雾模糊图像。而目前存在双重退化因素图像的研究比较缺乏,因此提出了一种新的用于描述带雾模糊图像的退化过程的数学模型。该模型在传统的大气散射模型和模糊退化模型的基础上,以带雾模糊图像的实际生成过程作为依据进行推导,可以描述双重退化因素导致的图像退化过程。使用该模型可以复原序列带雾模糊图像,实验结果表明复原效果良好。     

基于估计点扩展函数值的湍流退化图像复原

提出了一种直接从湍流退化图像中估计湍流点扩展函数值的方法.本方法不再利用自 然或人工向导星图像来测定点扩展函数,而是直接利用两帧连续短曝光湍流退化图像作为输入， 在空域中对其进行适当的延拓,在频域中建立和选择关于湍流点扩展函数离散值的一系列计算 方程.为了克服噪声的干扰,在点扩展函数的非负性和空间光滑性的约束条件下,将点扩展函数 的计算问题转化为优化估计问题,通过极小化准则函数估计点扩展函数值,进而恢复退化图像. 实验结果表明,本文方法十分有效,复原效果好.     

大气湍流下退化序列图像的目标检测方法

为解决大气湍流退化序列中运动目标检测困难的问题,提出了一种结合低秩分解和检测融合的目标检测方法。首先,根据退化视频中湍流运动分量的稀疏分布特点,采用低秩矩阵描述法将每帧图像分解为低秩稳像和稀疏运动两部分,初步实现场景和湍流运动的粗分离。其次,由于稀疏部分中包含目标在内的整个场景的稀疏运动量,引入自适应阈值法剔除干扰量,分割目标并填补其中空洞;对于无湍流偏移干扰的低秩部分,采用高斯建模获得低秩中的前景区域。最后,对两部分检测结果进行联合判定,从而获得准确的目标检测结果。实验表明,本文方法目标提取的准确度较高,明显优于当前经典检测方法,在强湍流条件下检测结果仍较为理想。     

基于双重循环交替迭代的湍流退化图像复原

湍流退化图像复原是一个富有挑战性的世界性难题,在航天光电成像领域具有广泛的应用前景。为了从少数几帧湍流退化图像中将目标图像有效地恢复出来,提出一种新颖的基于双重循环交替迭代的湍流退化图像复原算法,建立了基于内外循环交替求解目标图像及各帧点扩展函数的迭代关系。在微机上进行了一些复原实验,实验结果表明该算法能用少数几帧图像获取目标图像和点扩展函数的最佳联合估计,证实了算法的可行性和实用价值。     

Level Set Curve Matching and Particle Image Velocimetry for Resolving Chemistry and Turbulence Interactions in Propagating Flames

We present an imaging, image processing, and image analysis framework for facilitating the separation of flow and chemistry effects on local flame front structures. Image data of combustion processes are obtained by a novel technique that combines simultaneous measurements of distribution evolutions of OH radicals and of instantaneous velocity fields in turbulent flames. High-speed planar laser induced fluorescence (PLIF) of OH radicals is used to track the response of the flame front to the turbulent flow field. Instantaneous velocity field measurements are simultaneously performed using particle image velocimetry (PIV). Image analysis methods are developed to process the experimentally captured data for the quantitative study of turbulence/chemistry interactions. The flame image sequences are smoothed using nonlinear diffusion filtering and flame boundary contours are automatically segmented using active contour models. OH image sequences are analyzed using a curve matching algorithm that incorporates level sets and geodesic path computation to track the propagation of curves representing successive flame contours within a sequence. This makes it possible to calculate local flame front velocities, which are strongly affected by turbulence/chemistry interactions. Since the PIV data resolves the turbulent flow field, the combined technique allows a more detailed investigation of turbulent flame phenomena.     

Estimating the contribution of different parts of the atmosphere to optical wavefront aberration

Current adaptive optical telescope designs use a single deformable mirror (DM), usually conjugated to the aperture plane, to compensate for the cumulative effects of optical turbulence. The corrected field of view (FOV) of an adaptive optics system could theoretically be increased through the use of multiple DMs conjugated to a like number of corresponding planes which sample the turbulence region in altitude. Often, the atmospheric turbulence responsible for the degradation of long-exposure telescope images is concentrated in several relatively strong layers. The logical location for the planes of correction in a multiconjugate adaptive optics (MCAO) system would be the same as these “seeing layers.” Each DM would correct for the component of the total wavefront contributed by its associated turbulent layer. However, there is no known method of isolating a particular layer so that its component may be measured. Somehow, the individual components must be estimated using available measurements of the cumulative wavefront at the aperture of the telescope. This paper presents a theoretical analysis of a signal processing technique for determining these phase contributions. The method takes advantage of the spatial diversity of wavefront sensor (WFS) measurements from two or more reference sources. These separate wavefront sensor measurements are processed via minimum mean square error filtering to yield an estimate of the phase perturbation caused by a particular turbulent layer of the atmosphere. Our results indicate that multiple wavefront corrector adaptive optics systems will require much brighter reference sources than single wavefront corrector systems.     

一种大气湍流环境下的运动目标自适应检测方法

在远距离成像过程中,图像序列受到湍流的影响会出现像素点亮度的随机起伏、闪烁和图像中物体的位置漂移,这使得传统的背景建模方法在湍流环境下难以准确检测运动目标.针对图像受到湍流影响在不同区域表现出的不同性质,提出分层次决策判别方法.首先,用高斯模型建模背景平坦区域,用双高斯模型建模背景中的物体边缘区域,设定判别式对每个像素点进行判别,并在线更新模型参数;然后,针对由于湍流影响出现的亮度突变点建立自适应判别模型,结合前一层次的判别结果,构造判别条件消除亮度突变点,分割得到目标点;最后,通过连通区域约束得到目标区域.实验结果表明,本文方法在不同湍流强度下对不同数量和不同运动方向的目标取得了良好的检测效果.     

Removing atmospheric MTF and establishing an MTF compensation filter for the HJ-1A CCD camera

The total image modulation transfer function (MTF) comprises imaging system MTF and atmospheric MTF, both of which can produce blurring effects in remote-sensing images. Imaging system MTF, one of the important specifications of the imaging system, is used to characterize the radiometric response of the sensor. Atmospheric MTF, which consists of aerosol MTF and turbulence MTF, usually changes with external atmospheric conditions. To obtain the MTF of the imaging system, the atmospheric MTF has to be removed from the total image MTF. This study aims to determine a method for removing the atmospheric MTF for the charge-coupled device (CCD) camera on-board the Huanjing 1A satellite (HJ-1A). The aerosol MTF and the turbulence MTF in the CCD images were first predicted based on the aerosol optical depth (AOD) derived by dark target and reanalysis of data from the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR). Then, the MTFs of the CCD camera were retrieved by removing the aerosol and turbulence MTFs from the total on-orbit image MTFs that were calculated from the linear features on CCD images. For the HJ-1A CCD camera, results show that the MTF values at Nyquist frequency increased by about 10% after the atmospheric MTFs were removed. The total image MTF became degraded by about 50% when AOD was high or turbulence was strong. Furthermore, this study has established an MTF compensation (MTFC) filter for the HJ-1A by modifying the ‘modified inverse filter (MIF)’ approach. Results illustrate that the effective instantaneous field of view (EIFOV) of the restored image improved from 78 to 41 m, while the noise level of the model was well controlled. This research demonstrates that the modified approach is effective for sensors with low MTF values.     

Atmospheric scattering and turbulence modulation transfer function for CCD cameras on CBERS-02b and HJ-1A/1B

Atmospheric turbulence and aerosol scattering can produce the blurring of the remotely sensed image. The degrading effect is usually quantified by atmospheric modulation transfer function (MTF). However, this effect behaves differently with different remote sensors. The effort of this article is to study the different degrading effects of aerosol MTF and turbulence MTF between charge-coupled device (CCD) camera on China–Brazil Earth Resources Satellite-02b (CBERS-02b) and on Huan Jing-1A/1B satellite (HJ-1A/1B). Specifically, a corrected aerosol MTF model is established based on classical solution of small-angle approximation (SAA) model by considering the MTF and the effective instantaneous field of view (EIFOV) of CCD cameras. By assuming many different atmospheric conditions, the aerosol MTF and turbulence MTF for two CCD cameras are evaluated. It is found that the output aerosol MTF of CCD camera on HJ-1A/1B causes more degrading effect than that of CBERS-02b under the same atmospheric condition. However, the situation reverses for the turbulence MTF. Furthermore, CCD images acquired over Beijing, China, by CBERS-02b and HJ-1A/1B on four different dates are selected. The overall atmospheric MTF for these images are determined based on the aerosol products from Aerosol Robotic Network (Aeronet) and radiosounding data. Results indicate that the overall atmospheric MTF of CBERS-02b CCD camera reduces image quality more seriously than that of HJ-1A/1B CCD camera. Additionally, the atmospheric MTF compensation is performed and evaluated for these CCD images based on the overall atmospheric MTF.     

An investigation of pulsating turbulent open channel flow by large eddy simulation

Pulsating turbulent open channel flow is investigated by use of large eddy simulation (LES) technique coupled with a dynamic subgrid-scale (SGS) model for turbulent SGS stress. The three-dimensional filtered Navier-Stokes equation is numerically solved by a fractional-step method. The objective of this study is to deal with the behaviors of pulsating turbulent open channel flow, in particular turbulence characteristics in the free surface-influenced layer, and to examine the reliability of the LES approach for predicting the pulsating turbulent flow with a free surface. In this study, the frequency of driving pressure gradient ranges low, medium and high value. The mean and phase-averaged statistical turbulence quantities, the resolved turbulent kinetic energy and Reynolds stresses budgets, and the flow structures are obtained and analyzed. With the increase of the driving frequency, the depth of the surface-influenced layer increases and the turbulent Stokes length near the bottom wall decreases. Different turbulence characteristics between the accelerating and decelerating phases are interpreted comprehensively. Turbulence intensities reveal that turbulent flow has a strong anisotropy in the free surface-influenced layer, in particular in the decelerating phases during the pulsating cycle. The budget terms of the resolved turbulent kinetic energy, the vertical and spanwise Reynolds stresses in the free surface region are analyzed. The flow structures clearly exhibit that bursting processes near the bottom wall are ejected toward the surface and the most surface renewal events are closely correlated with the bursting processes. These processes are strengthened during the decelerating period since strong turbulence intensities are generated.     

电波传播对L波段地球同步轨道圆迹SAR重轨干涉性能的影响

不同于常规星载SAR,L波段地球同步轨道圆迹SAR(GEOCSAR)轨道高度高、合成孔径时间长、观测面积大,对流层折射率和电离层电子含量的时空变化将严重影响GEOCSAR重轨干涉性能。基于大气介质时空变化及GEOCSAR干涉信号的特点,从对流层和电离层相位延迟误差、电离层水平变化引起的图像方位向偏移、对流层折射率周日变化和电离层时空随机起伏引起的方位向聚焦性能下降、以及法拉第旋转效应几个方面分析了对流层和电离层对GEOCSAR重轨干涉相干系数、干涉相位的影响,并进行了仿真实验验证。分析以及实验结果表明,对流层和电离层均会对GEOCSAR干涉性能造成不可忽略的影响,其中电离层的周日时间变化和电离层起伏会导致图像散焦,进而严重降低干涉相干性。     

Minimum‐variance control of astronomical adaptive optic systems with actuator dynamics under synchronous and asynchronous sampling

Adaptive optic (AO) systems are now routinely used in ground‐based telescopes to counter the effects of atmospheric turbulence. A deformable mirror (DM) generates a correction wavefront, which is subtracted from the turbulent wavefront using measurements of the residual phase provided by a wavefront sensor (WFS). Minimizing the variance of the residual phase defines a sampled data control problem combining a continuous time minimum‐variance (MV) performance criterion with a discrete‐time controller. For a fairly general class of linear time‐invariant DM and turbulence WFS models, this control problem can be transformed into an equivalent discrete‐time LQ optimization problem involving a set of (discrete‐time) control‐sufficient statistics of the incoming continuous‐time turbulence. This paper shows how to constructively solve this MV problem in the presence of DM's dynamics, starting from continuous‐time models of DM and turbulence. This result is extended to the case of asynchronous DM/WFS sampling. An illustrative application to optimal control of tip‐tilt turbulent modes for the European extremely large telescope in the presence of first‐order DM's dynamics is presented. Copyright © 2010 John Wiley & Sons, Ltd.