An algebraic algorithm for nonuniformity correction in focal-plane arrays

A scene-based algorithm is developed to compensate for bias nonuniformity in focal-plane arrays. Nonuniformity can be extremely problematic, especially for mid- to far-infrared imaging systems. The technique is based on use of estimates of interframe subpixel shifts in an image sequence, in conjunction with a linear-interpolation model for the motion, to extract information on the bias nonuniformity algebraically. The performance of the proposed algorithm is analyzed by using real infrared and simulated data. One advantage of this technique is its simplicity; it requires relatively few frames to generate an effective correction matrix, thereby permitting the execution of frequent on-the-fly nonuniformity correction as drift occurs. Additionally, the performance is shown to exhibit considerable robustness with respect to lack of the common types of temporal and spatial irradiance diversity that are typically required by statistical scene-based nonuniformity correction techniques.     

Radiometrically accurate scene-based nonuniformity correction for array sensors

A novel radiometrically accurate scene-based nonuniformity correction (NUC) algorithm is described. The technique combines absolute calibration with a recently reported algebraic scene-based NUC algorithm. The technique is based on the following principle: First, detectors that are along the perimeter of the focal-plane array are absolutely calibrated; then the calibration is transported to the remaining uncalibrated interior detectors through the application of the algebraic scene-based algorithm, which utilizes pairs of image frames exhibiting arbitrary global motion. The key advantage of this technique is that it can obtain radiometric accuracy during NUC without disrupting camera operation. Accurate estimates of the bias nonuniformity can be achieved with relatively few frames, which can be fewer than ten frame pairs. Advantages of this technique are discussed, and a thorough performance analysis is presented with use of simulated and real infrared imagery.     

红外焦平面阵列非均匀性非线性校正新方法

针对红外焦平面阵列非均匀性线性校正方法存在较大误差,而考虑非线性响应的校正算法又过于复杂,难于在实际工程中获得运用等难题,本丈提出一种易于硬件实时处理、校正精度较高的红外焦平面阵列非均匀性非线性校正新方法.在介绍非线性校正新方法原理的基础上,推导出其数学模型,并给出实验结果.实验结果表明该校正方法的校正精度达到1.2%,校正过程中需要存储的参数仅为4个/1象素,易于硬件实现实时校正.     

Scene-based nonuniformity correction algorithm based on interframe registration

In this paper, we present a simple and effective scene-based nonuniformity correction (NUC) method for infrared focal plane arrays based on interframe registration. This method estimates the global translation between two adjacent frames and minimizes the mean square error between the two properly registered images to make any two detectors with the same scene produce the same output value. In this way, the accumulation of the registration error can be avoided and the NUC can be achieved. The advantages of the proposed algorithm lie in its low computational complexity and storage requirements and ability to capture temporal drifts in the nonuniformity parameters. The performance of the proposed technique is thoroughly studied with infrared image sequences with simulated nonuniformity and infrared imagery with real nonuniformity. It shows a significantly fast and reliable fixed-pattern noise reduction and obtains an effective frame-by-frame adaptive estimation of each detector's gain and offset.     

Scene-based nonuniformity correction for focal plane arrays by the method of the inverse covariance form

What is to our knowledge a new scene-based algorithm for nonuniformity correction in infrared focal-plane array sensors has been developed. The technique is based on the inverse covariance form of the Kalman filter (KF), which has been reported previously and used in estimating the gain and bias of each detector in the array from scene data. The gain and the bias of each detector in the focal-plane array are assumed constant within a given sequence of frames, corresponding to a certain time and operational conditions, but they are allowed to randomly drift from one sequence to another following a discrete-time Gauss-Markov process. The inverse covariance form filter estimates the gain and the bias of each detector in the focal-plane array and optimally updates them as they drift in time. The estimation is performed with considerably higher computational efficiency than the equivalent KF. The ability of the algorithm in compensating for fixed-pattern noise in infrared imagery and in reducing the computational complexity is demonstrated by use of both simulated and real data.     

改进的基于卡尔曼滤波的非均匀性校正算法

本文研究的对象是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精度的影响,提出了改进的基于卡尔曼滤波的非均匀性校正算法.该算法的研究方法是采用探测器响应的分段线性模型,对基于卡尔曼滤波器非均匀性校正算法进行扩展和改进,其结果能有效地克服红外焦平面阵列探测单元响应特性的非线性对校正精度的影响.实验结论表明,改进后的算法在一定程度上解决了探测器偏置和增益随时间漂移以及响应非线性影响非均匀性校正性能的问题,获得了较好的非均匀性校正效果.     

Scene-based nonuniformity correction technique that exploits knowledge of the focal-plane array readout architecture

Spatial fixed-pattern noise is a common and major problem in modern infrared imagers owing to the nonuniform response of the photodiodes in the focal plane array of the imaging system. In addition, the nonuniform response of the readout and digitization electronics, which are involved in multiplexing the signals from the photodiodes, causes further nonuniformity. We describe a novel scene based on a nonuniformity correction algorithm that treats the aggregate nonuniformity in separate stages. First, the nonuniformity from the readout amplifiers is corrected by use of knowledge of the readout architecture of the imaging system. Second, the nonuniformity resulting from the individual detectors is corrected with a nonlinear filter-based method. We demonstrate the performance of the proposed algorithm by applying it to simulated imagery and real infrared data. Quantitative results in terms of the mean absolute error and the signal-to-noise ratio are also presented to demonstrate the efficacy of the proposed algorithm. One advantage of the proposed algorithm is that it requires only a few frames to obtain high-quality corrections.     

基于NIOSII的红外图像实时非均匀性校正

实时红外图像非均匀性校正是红外热成像系统的基础.采用Altera公司的NIOSII嵌入式软核CPU技术,提出了一种基于非线性拟合的实时校正方法以及利用FPGA为核心的硬件实现途径.针对校正中耗时的算法用FPGA中模块完成,采用NIOSII处理器的自定制指令,使FPGA的并行性和DSP的灵活性完美结合在一起,极大的提高了系统的性能.该方法动态范围大,系统仅采用一片FPGA芯片,使得系统小型化成为可能.介绍了硬件电路的原理图以及工作过程并给出实验结果.实验证明了这种方法的优越性,并取得了满意的实验结果.     

Optimized algorithm for the spatial nonuniformity correction of an imaging system based on a charge-coupled device color camera

We present an optimized linear algorithm for the spatial nonuniformity correction of a CCD color camera's imaging system and the experimental methodology developed for its implementation. We assess the influence of the algorithm's variables on the quality of the correction, that is, the dark image, the base correction image, and the reference level, and the range of application of the correction using a uniform radiance field provided by an integrator cube. The best spatial nonuniformity correction is achieved by having a nonzero dark image, by using an image with a mean digital level placed in the linear response range of the camera as the base correction image and taking the mean digital level of the image as the reference digital level. The response of the CCD color camera's imaging system to the uniform radiance field shows a high level of spatial uniformity after the optimized algorithm has been applied, which also allows us to achieve a high-quality spatial nonuniformity correction of captured images under different exposure conditions.     

Scene-based nonuniformity correction with video sequences and registration

We describe a new, to our knowledge, scene-based nonuniformity correction algorithm for array detectors. The algorithm relies on the ability to register a sequence of observed frames in the presence of the fixed-pattern noise caused by pixel-to-pixel nonuniformity. In low-to-moderate levels of nonuniformity, sufficiently accurate registration may be possible with standard scene-based registration techniques. If the registration is accurate, and motion exists between the frames, then groups of independent detectors can be identified that observe the same irradiance (or true scene value). These detector outputs are averaged to generate estimates of the true scene values. With these scene estimates, and the corresponding observed values through a given detector, a curve-fitting procedure is used to estimate the individual detector response parameters. These can then be used to correct for detector nonuniformity. The strength of the algorithm lies in its simplicity and low computational complexity. Experimental results, to illustrate the performance of the algorithm, include the use of visible-range imagery with simulated nonuniformity and infrared imagery with real nonuniformity.     

便携式 X-射线透视仪图像非均匀性分析及校正

从射线场强度的非均匀性、转换屏响应不一致性和 CCD 相机像元光电响应不一致性三方面对便携式X射线透视仪图像非均匀性的产生机理进行了理论分析. 并在此基础上建立了透视仪每个像元通道光电响应的数学模型. 基于该模型,提出了一种多点线性拟合校正算法. 给出了应用本方法校正前后的图像结果及标准差对比情况. 本方法已应用于便携式安检排爆 X-射线检测系统.     

Statistical algorithm for nonuniformity correction in focal-plane arrays.

A statistical algorithm has been developed to compensate for the fixed-pattern noise associated with spatial nonuniformity and temporal drift in the response of focal-plane array infrared imaging systems. The algorithm uses initial scene data to generate initial estimates of the gain, the offset, and the variance of the additive electronic noise of each detector element. The algorithm then updates these parameters by use of subsequent frames and uses the updated parameters to restore the true image by use of a least-mean-square error finite-impulse-response filter. The algorithm is applied to infrared data, and the restored images compare favorably with those restored by use of a multiple-point calibration technique.     

Statistical algorithm for nonuniformity correction in focal-plane arrays

A statistical algorithm has been developed to compensate for the fixed-pattern noise associated with spatial nonuniformity and temporal drift in the response of focal-plane array infrared imaging systems. The algorithm uses initial scene data to generate initial estimates of the gain, the offset, and the variance of the additive electronic noise of each detector element. The algorithm then updates these parameters by use of subsequent frames and uses the updated parameters to restore the true image by use of a least-mean-square error finite-impulse-response filter. The algorithm is applied to infrared data, and the restored images compare favorably with those restored by use of a multiple-point calibration technique.     

A technique of measuring and correcting emittance dilutions due to accelerator structure misalignments

This paper describes a method of reducing the transverse emittance dilution in linear colliders due to transverse wakefields arising from misaligned accelerator structures. The technique is a generalization of the Wake-Free [T.O. Raubenheimer, Nucl. Instr. and Meth. A 306 (1991) 61] correction algorithm. The structure errors are measured locally by varying the bunch charge and/or bunch length and measuring the change in the beam trajectory. The structure errors can then be corrected by varying the trajectory or moving the structures. The results of simulations are presented demonstrating the viability of the technique.     

Solution for the nonuniformity correction of infrared focal plane arrays

Based on the S-curve model of the detector response of infrared focal plan arrays (IRFPAs), an improved two-point correction algorithm is presented. The algorithm first transforms the nonlinear image data into linear data and then uses the normal two-point algorithm to correct the linear data. The algorithm can effectively overcome the influence of nonlinearity of the detector's response, and it enlarges the correction precision and the dynamic range of the response. A real-time imaging-signal-processing system for IRFPAs that is based on a digital signal processor and field-programmable gate arrays is also presented. The nonuniformity correction capability of the presented solution is validated by experimental imaging procedures of a 128 x 128 pixel IRFPA camera prototype.     

声强测量中系统误差修正的若干问题研究

本文对用残余声强法修正声强测量的系统误差进行了理论分析，推导了它的误差表达工式，给出了该误差的二元分布图。     

Comments on “A correction algorithm for detector nonuniformity in computed tomography”

In this paper we comment on the publication “A correction algorithm for detector nonuniformity in computed tomography” by Sun et al. (Nucl. Instr. and Meth. A 505 (2003) 552).     

Improved neural network based scene-adaptive nonuniformity correction method for infrared focal plane arrays

An improved scene-adaptive nonuniformity correction (NUC) algorithm for infrared focal plane arrays (IRFPAs) is proposed. This method simultaneously estimates the infrared detectors' parameters and eliminates the nonuniformity causing fixed pattern noise (FPN) by using a neural network (NN) approach. In the learning process of neuron parameter estimation, the traditional LMS algorithm is substituted with the newly presented variable step size (VSS) normalized least-mean square (NLMS) based adaptive filtering algorithm, which yields faster convergence, smaller misadjustment, and lower computational cost. In addition, a new NN structure is designed to estimate the desired target value, which promotes the calibration precision considerably. The proposed NUC method reaches high correction performance, which is validated by the experimental results quantitatively tested with a simulative testing sequence and a real infrared image sequence.     

On Network Designs with Coding Error Detection and Correction Application

The detection of error and its correction is an important area of mathematics that is vastly constructed in all communication systems. Furthermore, combinatorial design theory has several applications like detecting or correcting errors in communication systems. Network (graph) designs (GDs) are introduced as a generalization of the symmetric balanced incomplete block designs (BIBDs) that are utilized directly in the above mentioned application. The networks (graphs) have been represented by vectors whose entries are the labels of the vertices related to the lengths of edges linked to it. Here, a general method is proposed and applied to construct new networks designs. This method of networks representation has simplified the method of constructing the network designs. In this paper, a novel representation of networks is introduced and used as a technique of constructing the group generated network designs of the complete bipartite networks and certain circulants. A technique of constructing the group generated network designs of the circulants is given with group generated graph designs (GDs) of certain circulants. In addition, the GDs are transformed into an incidence matrices, the rows and the columns of these matrices can be both viewed as a binary nonlinear code. A novel coding error detection and correction application is proposed and examined.     

Multimodel Kalman filtering for adaptive nonuniformity correction in infrared sensors

We present an adaptive technique for the estimation of nonuniformity parameters of infrared focal-plane arrays that is robust with respect to changes and uncertainties in scene and sensor characteristics. The proposed algorithm is based on using a bank of Kalman filters in parallel. Each filter independently estimates state variables comprising the gain and the bias matrices of the sensor, according to its own dynamic-model parameters. The supervising component of the algorithm then generates the final estimates of the state variables by forming a weighted superposition of all the estimates rendered by each Kalman filter. The weights are computed and updated iteratively, according to the a posteriori-likelihood principle. The performance of the estimator and its ability to compensate for fixed-pattern noise is tested using both simulated and real data obtained from two cameras operating in the mid- and long-wave infrared regime.