New temporal high-pass filter nonuniformity correction based on bilateral filter

A thorough analysis of low convergence speed and ghosting artifacts in temporal high-pass filter correction has been undertaken in this paper and it has found out that the keys of these problems are the interference of a large sum of unrelated scene information in the nonuniformity correction (NUC) process. In order to overcome these drawbacks, a new scene-based NUC technique based on bilateral filter has been developed. This method separates the original input frames into two parts and it estimates the NUC parameters only by using the residuals. The experimental results have shown that it can significantly increase convergence speed and reduce ghosting artifacts.     

Space low-pass and temporal high-pass nonuniformity correction algorithm

Nowadays, almost all scene-based nonuniformity correction (NUC) algorithms have the same shortcoming: a low convergence speed. This shortcoming produces ghosting artifacts. In this paper, we will start by discussing the traditional temporal high-pass NUC algorithm, and then combine the space frequency and temporal frequency. This combination will produce a new algorithm called the space low-pass and temporal high-pass NUC algorithm. The kernel idea of this algorithm is to eliminate the nonuniformity’s high-space-frequency part and retain the nonuniformity’s low-space-frequency part. Moreover, the high-space-frequency part’s processing is controllable, which markedly increases convergence speed. These features make our algorithm’s convergence speed so high that processed images have almost no ghosting artifacts.     

Improved interframe registration based nonuniformity correction for focal plane arrays

In this paper, an improved interframe registration based nonuniformity correction algorithm for focal plane arrays is proposed. The method simultaneously estimates detector parameters and carries out the nonuniformity correction by minimizing the mean square error between the two properly registered image frames. A new masked phase correlation algorithm is introduced to obtain reliable shift estimates in the presence of fixed pattern noise. The use of an outliers exclusion scheme, together with a variable step size strategy, could not only promote the correction precision considerably, but also eliminate ghosting artifacts effectively. The performance of the proposed algorithm is evaluated with clean infrared image sequences with simulated nonuniformity and real pattern noise. We also apply the method to a real-time imaging system to show how effective it is in reducing noise and the ghosting artifacts.     

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。     

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.     

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.     

Fast iterative adaptive nonuniformity correction with gradient minimization for infrared focal plane arrays

A fast scene-based nonuniformity correction algorithm is proposed for fixed-pattern noise removal in infrared focal plane array imagery. Based on minimization of L0 gradient of the estimated irradiance, the correction function is optimized through correction parameters estimation via iterative optimization strategy. When applied to different real IR data, the proposed method provides enhanced results with good visual effect, making a good balance between nonuniformity correction and details preservation. Comparing with other excellent approaches, this algorithm can accurately estimate the irradiance rapidly with fewer ghosting artifacts.     

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.     

A new adaptive nonuniformity correction algorithm for infrared line scanner based on neural networks

The striping pattern nonuniformity of the infrared line scanner (IRLS) severely limits the system performance. An adaptive nonuniformity correction (NUC) algorithm for IRLS using neural network is proposed.It uses a one-dimensional median filter to generate ideal output of network and can complete NUC by a single frame with a high correction level. Applications to both simulated and real infrared images show that the algorithm can obtain a satisfactory result with low complexity, no need of scene diversity or global motion between consecutive frames. It has the potential to realize real-time hardware-based applications.     

Single image non-uniformity correction using compressive sensing

A non-uniformity correction (NUC) method for an infrared focal plane array imaging system was proposed. The algorithm, based on compressive sensing (CS) of single image, overcame the disadvantages of “ghost artifacts” and bulk calculating costs in traditional NUC algorithms. A point-sampling matrix was designed to validate the measurements of CS on the time domain. The measurements were corrected using the midway infrared equalization algorithm, and the missing pixels were solved with the regularized orthogonal matching pursuit algorithm. Experimental results showed that the proposed method can reconstruct the entire image with only 25% pixels. A small difference was found between the correction results using 100% pixels and the reconstruction results using 40% pixels. Evaluation of the proposed method on the basis of the root-mean-square error, peak signal-to-noise ratio, and roughness index (ρ) proved the method to be robust and highly applicable.     

Nonuniformity correction of infrared images based on infrared radiation and working time of thermal imager

The spatial fixed-pattern noise (FPN) reduces the quality of the infrared image seriously, even makes infrared images inappropriate for some applications. In order to lower the FPN, some critical nonuniformity correction (NUC) algorithms such as NUC based on linear model, scene-based NUC and so on have been developed. But the algorithms have some drawbacks: restricted application in small dynamic range of objects temperature, low performance under the drift with the working time and complex calculations. In these cases, we develop a bivariate and quadratic model (bivariate is radiation and working time) of the FPA and the NUC technique based on the model. The proposed method is a true reflection of the infrared response and is a good solution for hardware implementation. It overcomes the drawbacks of the critical algorithm mentioned above. The last simulations and experiments show that the proposed algorithm exhibits a superior correction effect in both large objects temperature range and long working time of the thermal imager.     

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.     

基于混合高斯模型的红外图像自适应校正算法

针对传统时域高通滤波校正算法存在的鬼影问题,提出一种新的基于混合高斯模型的红外图像自适应校正算法。新算法利用混合高斯模型对场景进行建模,只有在像元输出值满足一定条件的时候,才将其更新到校正系数中,实现有选择性地更新校正系数。通过一组仿真和真实的红外图像序列评价算法的性能,仿真图像采用峰值信噪比指标进行定量评价,新算法比传统时域高通滤波校正算法的峰值信噪比提高了约9 dB。真实图像采用主观的定性评价,传统算法校正结果中存在着明显的鬼影,而新算法校正结果中不存在鬼影。     

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。     

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。     

Registration method for infrared images under conditions of fixed-pattern noise

This paper proposes a new registration method for infrared images under conditions of fixed-pattern noise (FPN). Conventional registration techniques are susceptible to FPN and it is therefore very desirable to have a registration algorithm that is tolerant to FPN. For this purpose, we utilize the difference of the cross-power spectrum of two discrete shifted images to suppress the noise power spectrum while the shifts information is well preserved. In particular, we show that the phase of the cross-power spectrum difference is a periodic two-dimensional binary stripe signal with the exact shifts determined to subpixel accuracy by the number of periods of the phase difference along each frequency axis. Robust estimates of shifts can be obtained by transforming its discontinuities to Hough domain. Experimental results show that the proposed method exhibits robust and accurate registration performance even for the noisy images that could not be handled by conventional registration algorithms. We have also incorporated this technique to a registration-based nonuniformity correction (NUC) framework, indicating that our registration technique is able to estimate motion parameters reliably, leading to satisfactory NUC result.     

Analyzing diffusion tensor images with ghosting artifacts: the effects of direct and indirect normalization

The current study aims to assess the applicability of direct or indirect normalization for the analysis of fractional anisotropy (FA) maps in the context of diffusion-weighted images (DWIs) contaminated by ghosting artifacts. We found that FA maps acquired by direct normalization showed generally higher anisotropy than indirect normalization, and the disparities were aggravated by the presence of ghosting artifacts in DWIs. The voxel-wise statistical comparisons demonstrated that indirect normalization reduced the influence of artifacts and enhanced the sensitivity of detecting anisotropy differences between groups. This suggested that images contaminated with ghosting artifacts can be sensibly analyzed using indirect normalization.