基于自适应滤波的红外焦平面阵列非均匀性校正算法

 红外探测器响应漂移特性会降低红外焦平面阵列（IRFPA）非均匀性校正的精度。针对该问题提出了一种基于场景的IRFPA非均匀性校正算法。该算法利用所获得的序列成像场景信息,采用一种基于快速自适应滤波器的最优化递归估计方法来获得非均匀性校正参数,并利用当前的成像信息来更新校正参数,以此降低探测器响应漂移特性对非均匀性校正的影响。算法仿真实验显示,对非线性参数为26.12%的同一图像,使用该算法、两点校正算法和卡尔曼滤波校正算法校正1 h后,可分别将非线性参数降至1.856%,3.122%和1.893%,说明该算法可获得稳定而较好的非均匀性校正效果。     

红外焦平面联合非均匀性校正算法

分别分析了红外焦平面阵列(IRFPA)基于定标的非均匀性校正(NUC)算法和基于场景的NUC算法的优势和问题.在此基础上提出了联合NUC算法,其中利用基于定标的两点校正法来初步消除探测器的非均匀性,然后再采用基于场景的时域高通校正法和新型自适应滤波校正法来抑制探测器非均匀性参数漂移的影响,同时减弱系统噪声对成像质量的破...     

小波变换的红外焦平面阵列非均匀性校正算法

红外焦平面阵列(IRFPA)的非均匀性是其应用中必须解决的技术难题之一。基于小波理论,提出了一种基于成像场景的红外焦平面非均匀性校正算法。该算法选择合适的小波函数对红外成像序列进行小波分解,而后对分解的信号计算出相应的统计量,从而得出红外焦平面非均匀性校正的偏置和增益校正系数,以此最终实现非均匀性校正。对真实红外序列图像的处理效果验证了该算法可较好地实现非均匀性校正。此外该算法对慢变化量具有较好的自适应性,可较好地抑制一般基于场景统计的非均匀性校正算法中出现的"人工虚影"的现象。     

基于系统非线性的红外焦平面非均匀性校正

采用系统非线性描述和分析方法,从基于红外参考辐射源的校正算法原理入手,提出了一种新的红外焦平面非均匀性校正方法.该算法将每个探测器单元视为一个非线性系统,因此,该算法原理既对基于红外参考辐射源的非均匀性校正算法成立,对基于场景的校正算法在一定条件下也有效,能够适应非均匀性随时间和环境改变而发生缓慢变化的情形,是一种自适应算法.     

基于场景的红外焦平面阵列非均匀性校正算法综述

红外焦平面阵列的非均匀性噪声是制约红外成像质量的主要因素。基于场景的非均匀校正算法通常利用图像序列并依赖帧间运动对焦平面阵列的非均匀性进行校正。介绍和分析了全局非均匀性校正,Kalman滤波器法,自适应滤波法,轨迹跟踪法,基于场景运动分析的校正算法,基于小波变换实现低通滤波的校正算法,高通滤波与神经网络相结合的算法,基于小波去噪、序列图像配准和正交最小二乘拟合的校正算法,改进的神经网络算法以及代数算法等,对算法进行了比较。     

一种基于恒定统计的红外图像非均匀性校正算法

对红外焦平面阵列成像系统而言,基于场景的非均匀校正技术是处理固定图案噪声的关键技术。现有的非均匀校正算法主要被收敛速度和鬼像问题所限制。提出一种新的基于恒定统计算法的自适应场景非均匀校正技术。利用红外图像序列的时域统计信息结合提出的α修正均值滤波来估计探测器的参数,通过减少样本的渐进方差估计,完成成像系统的非均匀性校正。通过模拟和真实的非均匀性图像对算法的性能进行评价。实验结果表明,在继承恒定统计算法快速收敛的同时,图像峰值信噪比较恒定校正法及常系数α校正算法分别有44.5%和32.9%的提升,图像鬼像问题有明显改善。     

热成像模式及其图像处理技术的研究与应用

热成像技术具有广泛的应用领域,随着红外焦平面探测器及数字图像处理技术的发展,新型热成像模式及其图像处理技术成为国内外发展的重要方向。介绍了近年来研究的几个典型进展,其中改进的基于场景特征的时域高通与空域低通滤波结合的非均匀性校正方法能够有效滤除制冷热成像系统观察低温天空场景时的水波纹固定图案噪声,并在FPGA硬件平台上实现了算法移植;研制出红外分焦平面偏振片阵列,实现了中波制冷和长波非制冷红外焦平面探测器的耦合成像,并通过考虑偏振片效应的偏振成像模型,滤除光路中偏振片的辐射影响;研制出基于常规制冷长波红外焦平面探测器的超频高动态热成像系统,在FPGA实现了多积分时间图像融合-细节增强级联的HDR图像融合方法,实现了对高动态场景的实时成像;研究了“田”字型四孔径和“十”字型四/五孔径等三类紧凑型视场部分重叠仿生复眼热成像模式,研制了2套实验系统来验证该方法的有效性;提出一种基于双生成对抗网络的非配对热红外-可见光图像转换算法TIV-Net,该方法能够将热图像有效地转化为类彩色可见光图像,并在无人机等平台实现了不低于20 Hz的实时处理。以上具有创新性的技术突破或已获得应用或展现良好的应用...     

基于非线性模型的卡尔曼滤波非均匀性校正算法

针对红外焦平面阵列探测单元响应的非线性对非均匀性校正的影响,利用探测器响应的非线性模型,对卡尔曼滤波非均匀性校正算法进行扩展和改进,以有效地克服响应非线性对校正精度的影响.该算法先对图像进行非线性压缩,转换为线性图像,再利用线性模型下的卡尔曼滤波算法实施非均匀校正,然后对其进行取指数操作,即得到原图非均匀校正后的图像.实验结果表明,该算法不仅继承了原算法利用场景信息来最优地更新校正参数的估计,解决了探测器偏置和增益漂移对校正影响,而且还在一定程度上解决了响应非线性对校正性能的影响,从而获得了较好的非均匀性校正效果.     

基于衬底温度和贝叶斯估计的红外非均匀性校正（英）

分析了红外焦平面阵列(IRFPA)基于定标的非均匀性校正法(NUC)和基于场景的NUC算法各自的优势和问题,在此基础上提出了联合非均匀性校正方法。根据上电时刻焦平面衬底的温度值,从FLASH中提取事先存储的对应温度区间的增益和偏置校正参数,初步消除探测器的非均匀性。通过分析初步校正后图像残余非均匀性噪声的特性,提出了一种自适应非均匀性校正算法NSCT,对经过NSCT分解后的子带图像,利用贝叶斯阈值逐点进行信号方差和噪声方差估计,计算出残余非均匀性噪声后并加以去除。实验结果表明,该算法能有效提高校正精度,并具有更强的环境适应性。     

基于特征分解的红外焦平面非均匀性校正算法

针对目前红外焦平面自适应场景校正算法工程应用的局限,提出了一种基于红外焦平面非均匀性特征分解的场景校正算法。分析了红外焦平面非性匀性构成因素,把其中的高频部分分解成盲点、斑块、行列非均匀性等,把缓慢变化的低频部分分解成梯度渐变非均匀性;分别对各类非均匀性采用不同的校正算法;合并校正结果,得到校正后的图像。实验结果表明,该算法校正精度高、收敛速度快、抑止目标退化能力强,适合工程应用。     

An enhanced non-uniformity correction algorithm for IRFPA based on neural network

Influenced by detector materials’ non-uniformity, growth and etching techniques, etc., every detector’s responsivity of infrared focal plane arrays (IRFPA) is different, which results in non-uniformity of IRFPA. And non-uniformity of IRFPA generates fixed pattern noises (FPN) that are superposed on infrared image. And it may degrade the infrared image quality, which greatly limits the application of IRFPA. Non-uniformity correction (NUC) is an important technique for IRFPA. The traditional non-uniformity correction algorithm based on neural network and its modified algorithms are analyzed in this paper. And a new improved non-uniformity correction algorithm based on neural network is proposed in this paper. In this algorithm, the desired image is estimated by using three successive images in an infrared sequence. And blurring effect caused by motion is avoided by applying implicit motion detection and edge detection. So the estimation image is closer to real image than the estimation image estimated by other algorithms, which results in fast convergence speed of correction parameters. A comparison is made to these algorithms in this paper. And experimental results show that the algorithm proposed in this paper can correct the non-uniformity of IRFPA effectively and it prevails over other algorithms based on neural network.     

Nonuniformity correction algorithm based on adaptive filter for infrared focal plane arrays

Response nonuniformity is a key problem that influences the imaging performance of infrared focal plane arrays (IRFPA) imaging system. A parallel processing algorithm to adaptively estimate the nonuniformity correction (NUC) parameters for IRFPA is presented. In this algorithm, a bank of the adaptive filter is applied to adaptively estimate the NUC parameters for every detector in IRFPA. The infrared image sequences are input into the bank of adaptive filter. After certain times recursion calculations are executed frame-by-frame, then the optimal coefficients of the gain and the offset of detector in IRFPA are achieved. Then the NUC is fulfilled ultimately. The algorithm reduces the influence that the response drift with time imposed on NUC effectively, and achieves good NUC effect. It was validated by real experimental imaging procedures.     

Non-uniformity correction algorithm for IRFPA based on motion controllable micro-scanning and perimeter diaphragm strips

Non-uniformity correction is the key issue for the image quality improvement of infrared focal panel array (IRFPA) imaging. A non-uniformity correction (NUC) algorithm for IRFPA based on motion controllable micro-scanning platform and perimeter diaphragm strips is presented. We initially execute one-point calibration to the perimeter detectors, then based on controllable motion of adjacent frames, a special algebraic algorithm is proposed to transport the calibration of the perimeter detectors to those interior un-corrected ones. In this way, the bias parameter of the whole field of view (FOV) is calculated. The algorithm can be easily combined with sub-pixel imaging, thereby improving the quality of thermal imaging system (image spatial resolution and uniformity). All calculations are algebraic, with a low computation load. The algorithm can realize adaptive one point calibration without covering the central FOV rapidly. Experiments on simulated infrared data demonstrate that this algorithm requires only dozens of frames to obtain high quality corrections.     

An evaluation method based on absolute difference to validate the performance of SBNUC algorithms

Scene-based non-uniformity correction (SBNUC) algorithms are an important part of infrared image processing; however, SBNUC algorithms usually cause two defects: (1) ghosting artifacts and (2) over-correction. In this paper, we use the absolute difference based on guided image filter (AD-GF) method to validate the performance of SBNUC algorithms. We obtain a self-separation source using the improved guided image filter to process the input image, and use the self-separation source to obtain the space-high-frequency parts of the input image and the corrected image. Finally, we use the absolute difference between the two space-high-frequency parts as the evaluation result. Based on experimental results, the AD-GF method has better robustness and can validate the performance of SBNUC algorithms even if ghosting artifacts or over-correction occur. Also the AD-GF method can measure how SBNUC algorithms perform in the time domain, it’s an effective evaluation method for SBNUC algorithm.     

Nonuniformity correction algorithm with nonlinear model for infrared focal plane arrays

For the detector in infrared focal plane arrays (IRFPA) with a large dynamic range of response, a nonlinear model of response curve of the detector in IRFPA is introduced in this paper. With the model, the Kalman-filter nonuniformity correction (NUC) algorithm with linear model, developed by Torres and Hayat, is extended. In the extended algorithm, the raw image is translated into a linearized one firstly by directly employing a logarithm-based transformation. Then the linearized image is corrected by the Kalman-filter NUC algorithm with linear model, and the corrected linearized image is obtained. Finally the uniformity image of the original one is achieved by fulfilling an exponent transformation to the corrected linearized image. The presented algorithm not only inherits the advantage of the original algorithm that resolves the problem of the temporal drift in the gain and the bias in each detector by updating NUC parameters with information of the current scene, but also reduce the influence of the detector nonlinear response to the NUC performance, so it is suitable for IRFPA under large response-range. The NUC ability of the presented algorithm is demonstrated by experiments with real infrared image sequences.     

Minimum mean square error method for stripe nonuniformity correction

Stripe nonuniformity is very typical in line infrared focal plane (IRFPA) and uncooled starring IRFPA. We develop the minimum mean square error (MMSE) method for stripe nonuniformity correction (NUC). The goal of the MMSE method is to determine the optimal NUC parameters for making the corrected image the closest to the ideal image. Moreover, this method can be achieved in one frame, making it more competitive than other scene-based NUC algorithms. We also demonstrate the calibration results of our algorithm using real and virtual infrared image sequences. The experiments verify the positive effect of our algorithm.     

热释电IRFPA非均匀性校正实验系统研究

采用虚拟仪器技术,设计了基于LabVIEW软件平台的热释电IRFPA（红外焦平面阵列）非均匀性校正系统。该系统可通过控制面板选择校正算法,选取3种不同的标准像元的标准校正曲线。可以对IRFPA待校正的像元输出进行采集,对非均匀性参数进行测试,还可以进行非均匀性校正。系统通过三维波形以及图像的显示来观察校正前后对比,并能计算出校正前后的NU（非均匀性）值大小。对像元数为120160的热释电IRFPA输出的视频信号进行了非均匀性校正实验,对非均匀性校正算法进行了统计对比,对仿真结果及数据进行了分析总结。经过校正实验验证了系统的可行性,结果证明基于平均值法的两点定标算法对热释电IRFPA非均匀性校正后的NU值是最小的。通过统计数据得出校正后的NU可平均下降30%。     

Improvement in adaptive nonuniformity correction method with nonlinear model for infrared focal plane arrays

The scene adaptive nonuniformity correction (NUC) technique is commonly used to decrease the fixed pattern noise (FPN) in infrared focal plane arrays (IRFPA). However, the correction precision of existing scene adaptive NUC methods is reduced by the nonlinear response of IRFPA detectors seriously. In this paper, an improved scene adaptive NUC method that employs “S”-curve model to approximate the detector response is presented. The performance of the proposed method is tested with real infrared video sequence, and the experimental results validate that our method can promote the correction precision considerably.