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.     

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。     

基于三次样条插值的IRFPA非均匀性校正算法*

针对红外成像制导跟踪系统工程应用的实际要求,对红外焦平面阵列工作在大动态范围条件下的非均匀性校正算法进行了深入研究,依据函数插值原理,导出了三次样条插值非均匀性校正算法.用模拟的非均匀性图像和实际的红外图像对算法进行了校验.结果表明该算法具有动态范围大、校正准确度高的优点,可对红外焦平面阵列实现非均匀性和非线性双重校正效果.     

基于三次样条插值的IRFPA非均匀性校正算法

针对红外成像制导跟踪系统工程应用的实际要求,对红外焦平面阵列工作在大动态范围条件下的非均匀性校正算法进行了深入研究,依据函数插值原理,导出了三次样条插值非均匀性校正算法.用模拟的非均匀性图像和实际的红外图像对算法进行了校验.结果表明该算法具有动态范围大、校正准确度高的优点,可对红外焦平面阵列实现非均匀性和非线性双重校正效果.     

基于变积分时间的红外焦平面非均匀性校正算法研究

针对两点温度定标算法在应用过程中曝露的问题,提出了基于变积分时间的红外焦平面非均匀性校正算法.该算法先对图像进行非线性压缩,转换为线性图像,再利用红外焦平面阵列探测元的响应特性与积分时间之间的关系,采用改变积分时间的方法拟合红外焦平面探测器的平均响应特性曲线,进行两点校正,然后对结果进行取指数操作,即得到原图非均匀校正后的图像.分别利用两点温度定标法和变积分法对航拍红外图像进行校正效果验证,同时进行了不同校正算法的非均匀性适应性评价实验.实验结果表明新算法计算量小,校正准确度高,反应速度快,并在一定程度上解决了大动态范围下响应非线性对校正性能的影响,具有很好的工程应用价值.     

一种具有自适应非均匀校正功能的非制冷焦平面探测器组件

基于场景的非均匀校正算法(scene based nonuniformity correction,SBNUC)是非均匀校正技术今后的重点发展方向,介绍了近年来基于恒定统计约束的SBNUC、神经网络的SBNUC和运动估计的SBNUC算法的研究进展。研究了SBNUC算法在实际焦平面探测器组件上的实现方法,该方法仅依赖拍摄序列的信息对焦平面探测器的增益和偏置参数进行组间更新或帧间更新,可有效补偿温漂。研制了一种具有自适应非均匀校正功能的非制冷焦平面探测器组件,红外视频经该组件处理后,图像质量有所提高。该组件可明显提高热成像系统的成像性能,并能动态地保证热成像系统随场景变化的稳定性。     

Scene based nonuniformity correction based on block ergodicity for infrared focal plane arrays

This paper puts forward a new scene based nonuniformity correction algorithm for IRFPA. This method adopts phase-correlation method for motion estimation and takes the sum of mean-square errors of the pixel brightness between several adjacent frames as the cost function when the brightness constancy assumption between two adjacent frames is satisfied. Nonuniformity correction parameters could be estimated by minimizing such cost function. In order to reduce calculation quantity, we can divide these images into several subblocks, and solve for the optimum solution of the cost function respectively in each subblock. From the analysis, it is shown that the optimum solution is of global uniqueness when all the elements in subblocks could satisfy the ergodicity condition. Then the estimated value of nonuniformity correction parameters could be deduced by minimizing the cost functions. The nonuniformity correction experiments for both infrared image sequence with simulated nonuniformity and infrared imagery with real nonuniformity show that the proposed algorithm could achieve a great correction effect by only analyzing a small number of frames.     

Guided filter and adaptive learning rate based non-uniformity correction algorithm for infrared focal plane array

Imaging non-uniformity of infrared focal plane array (IRFPA) behaves as fixed-pattern noise superimposed on the image, which affects the imaging quality of infrared system seriously. In scene-based non-uniformity correction methods, the drawbacks of ghosting artifacts and image blurring affect the sensitivity of the IRFPA imaging system seriously and decrease the image quality visibly. This paper proposes an improved neural network non-uniformity correction method with adaptive learning rate. On the one hand, using guided filter, the proposed algorithm decreases the effect of ghosting artifacts. On the other hand, due to the inappropriate learning rate is the main reason of image blurring, the proposed algorithm utilizes an adaptive learning rate with a temporal domain factor to eliminate the effect of image blurring. In short, the proposed algorithm combines the merits of the guided filter and the adaptive learning rate. Several real and simulated infrared image sequences are utilized to verify the performance of the proposed algorithm. The experiment results indicate that the proposed algorithm can not only reduce the non-uniformity with less ghosting artifacts but also overcome the problems of image blurring in static areas.     

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.     

HgCdTe-IRFPA成像器件光谱响应率标定方法比较与误差分析

红外焦平面成像器件的光谱响应率是天基红外遥感的基本物理指标。为了准确应用该项参数去除器件在制造工艺中的不均匀性对产品质量的影响，必须在系统使用之前对其重新标定，获取真实值。总结了目前较为普遍的IRPFA产品光谱响应率标定方法，通过比较选择标准代替法对产品进行标定。根据所得到测量数据，分析了其可能存在的误差，总结了在对IRPFA产品进行标定时应注意的问题。同时提出了一种调整积分时间的方法，以弥补有些波段标定黑体辐射功率过低带来误差过大的缺陷。     

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.     

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。     

一种基于DSP+CPLD的低功耗实时红外成像系统实现方法

非致冷红外焦平面阵列(IRFPA)固有的非均匀性严重制约了系统成像质量,其输出动态范围大和监视器显示输出动态范围小也构成了矛盾。为解决这些问题,采用了复杂可编程逻辑器件(CPLD)设计红外焦平面阵列输出驱动和视频编码芯片的显示时序,以高速低功耗数字信号处理器(DSP)为核心,实现了嵌入式红外图像实时采集与压缩校正硬件系统。提出了一种结合两点校正的灰度直方图统计阈值分段线性压缩变换算法,并进行了实验。结果表明,系统可以实时的对红外图像进行采集和压缩校正,处理后图像的非均匀性得到很好的校正,背景被抑制的同时目标和细节得到了增强。该方案简单可靠,电路功耗较低,可为小型化红外热像仪的研制提供参考。     

基于场景的红外焦平面阵列非均匀性评价方法

针对红外遥感图像非均匀性的定量评价问题,研究了基于场景的红外焦平面阵列响应非均匀性的定量评价方法。利用真实红外遥感图像序列量值分布的特点,针对小序列图像提出了基于序列排序和小波变换的标准图像构建算法;结合非均匀性评价指标,提出了利用构建标准图像的响应非均匀性对红外图像的非均匀性进行定量估计;分别利用黑体定标实验和统计方法对标准图像构建算法进行了验证,同时进行了不同校正算法的剩余非均匀性评价实验。结果表明,128frame序列构建得到的标准图像的非均匀性分别比同温黑体图像小0.92%,比统计标准图像大0.59%。该方法可用于定量估计红外遥感图像的非均匀性。     

A New Piecewise Approach for Nonuniformity Correction in IRFPA

Considering different detector in IRFPA has different nonlinear response characteristic, a novel piecewise linear connection scheme for nonuniformity correction in IRFPA is proposed in this paper. Comparing to the widely used piecewise linear correction, the proposed scheme tries to find the n-segment piecewise straight-line which strives for going near (not always through) all available calibration points, rather than just passes through some n + 1 calibration points. Since each detector's output is corrected to the expected correction value as near as possible, the new scheme can achieve better NUC performance within the whole calibration range than that of the old one. The comparison experiment with simulated data and real IRFPA infrared data shows that the performance of this approach is more perfect than that of the old piecewise linear algorithm.     

函数拟合红外焦平面阵列非均匀性校正FPGA实现研究

结合红外焦平面阵列（Infrared Focal Plane Array,IRFPA）非均匀性校正的工程实际,设计了基于函数拟合的校正算法,采用大容量高速现场可编程门阵列（Field Programmable Gate Array,FPGA）器件,实现了该非均匀性校正系统,它能有效适应红外焦平面阵列器件响应特性的大动态范围和非线性,具有体积小、运算速度快和校正准确度高等优点.     

基于自适应扩散模型的单帧红外条纹非均匀性校正算法

针对红外焦平面成像系统存在列向条纹非均匀性的现象,采用了一种基于自适应PM扩散模型的非均匀校正新算法。首先,综合利用图像梯度信息和局部灰度统计信息,自适应计算PM模型的扩散阈值;然后将每列像素的PM模型估计值作为该列像素的期望值;最后采用最陡下降法迭代计算得到每列像元的校正参数,并对结果进行循环校正以提高校正效果。实验结果表明:该算法可以保护图像边缘信息,与同类算法相比,能够更有效地抑制条纹非均匀性,并且能够防止图像产生鬼影。     

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.     

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。     

基于维纳滤波的红外焦平面阵列非均匀性校正算法

充分利用红外图像的空间相关性和时间相关性,提出了一种基于维纳（Wiener）滤波的红外焦平面阵列（IRFPA）非均匀性校正算法,实现对辐射信号的无偏估计,滤波器的参量通过时域一、二阶统计结合空域平均获得,同现有的典型校正算法相比,本文算法具有收敛速度快、校正准确度高的特点,理论分析以及针对实际红外图像的实验结果表明提出的算法更具优越性.