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.     

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.     

Kalman filtering for adaptive nonuniformity correction in infrared focal-plane arrays

A novel statistical approach is undertaken for the adaptive estimation of the gain and bias nonuniformity in infrared focal-plane array sensors from scene data. The gain and the bias of each detector are regarded as random state variables modeled by a discrete-time Gauss-Markov process. The proposed Gauss-Markov framework provides a mechanism for capturing the slow and random drift in the fixed-pattern noise as the operational conditions of the sensor vary in time. With a temporal stochastic model for each detector's gain and bias at hand, a Kalman filter is derived that uses scene data, comprising the detector's readout values sampled over a short period of time, to optimally update the detector's gain and bias estimates as these parameters drift. The proposed technique relies on a certain spatiotemporal diversity condition in the data, which is satisfied when all detectors see approximately the same range of temperatures within the periods between successive estimation epochs. The performance of the proposed technique is thoroughly studied, and its utility in mitigating fixed-pattern noise is demonstrated with both real infrared and simulated imagery.     

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.     

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.     

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.     

Two-color thermal detector with thermal chopping for infrared focal-plane arrays

Micromachined thermal infrared (IR) detectors are emerging into the marketplace to provide high-performance thermal (IR) imagery at low cost. Thermal detectors can be improved when a tunable wavelength response is provided and when a thermal chopper is incorporated into the detector by use of microelectromechanical (MEM) elements. Most thermal detectors require a chopper, continuous synchronous chopping in the case of pyroelectric detectors, or asynchronous chopping in the case of staring microbolometers. Mechanical choppers are bulky and costly. We present the fundamental principles of micromachined thermal detectors that possess tunable wavelength or color response and a technique for thermal chopping. A micromirror, switching between two spatial positions under the detector, provides a response to two wavelength windows by tuning the optical resonant cavity. The image can then be integrated at the readout level to achieve a multicolor IR picture. A thermal MEM chopper can be used instead of a mechanical chopper to maintain the same video frame rate and to allow for an interlaced resetting of staring thermal arrays. Unlike the second generation of uncooled IR arrays, the actual temperature of objects can be obtained by a comparison of the response in two wavelength windows, in addition to the direct measurement of IR power that they radiate in the entire 8-14-mum spectral region.     

Concentrators for focal-plane arrays

It is generally assumed that lenslet arrays are needed to concentrate image light upon the relatively sparse detector areas in a focal-plane array. These have severe field-of-view difficulties and require considerable space to implement. We show how to manufacture optically parallel arrays of concentrators of controllable size and field of view with photorefractive crystals.     

Adaptive convergence nonuniformity correction algorithm

Nowadays, convergence and ghosting artifacts are common problems in scene-based nonuniformity correction (NUC) algorithms. In this study, we introduce the idea of space frequency to the scene-based NUC. Then the convergence speed factor is presented, which can adaptively change the convergence speed by a change of the scene dynamic range. In fact, the convergence speed factor role is to decrease the statistical data standard deviation. The nonuniformity space relativity characteristic was summarized by plenty of experimental statistical data. The space relativity characteristic was used to correct the convergence speed factor, which can make it more stable. Finally, real and simulated infrared image sequences were applied to demonstrate the positive effect of our algorithm.     

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.     

Generalized algebraic scene-based nonuniformity correction algorithm

A generalization of a recently developed algebraic scene-based nonuniformity correction algorithm for focal plane array (FPA) sensors is presented. The new technique uses pairs of image frames exhibiting arbitrary one- or two-dimensional translational motion to compute compensator quantities that are then used to remove nonuniformity in the bias of the FPA response. Unlike its predecessor, the generalization does not require the use of either a blackbody calibration target or a shutter. The algorithm has a low computational overhead, lending itself to real-time hardware implementation. The high-quality correction ability of this technique is demonstrated through application to real IR data from both cooled and uncooled infrared FPAs. A theoretical and experimental error analysis is performed to study the accuracy of the bias compensator estimates in the presence of two main sources of error.     

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.     

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.     

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).     

一种改进的红外焦平面非均匀性校正算法

针对红外焦平面探测器现有非均匀性校正算法在实际应用中存在的问题,结合实际系统的开发,提出了一种改进算法——一点加两点校正算法。先求两点校正算法的校正增益和校正偏置,再求一点校正算法的校正偏置,求取一点校正算法的偏置参数时的图像数据来自前面求得的两点校正之后的数据,即最后的校正结果是两点校正后的再校正。理论分析和应用表明该算法与目前流行的算法相比具有实时性好、误差小、处理效果好等特点。     

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.     

微测辐射热计的非均匀性校正新方法

针对微测辐射热计的非均匀性校正对衬底温度要求较高的问题,从探测器的线性模型出发,提出了一种新型非均匀性校正方法。方法首先令阵列中不同探测单元的光响应率比和衬底温度响应率比分别相等,达到补偿衬底温度变化的目的;随后再执行传统的两点非均匀性校正。用数模转换器将存储在EPROM内的偏置电压输出到MOS管的栅极上,实现对偏置电流的控制,调节探测元及补偿元的响应率。仿真结果表明,该校正方法可以在变化范围约为4K的均匀衬底温度内达到良好校正效果。     

Comparison of designs of off-axis Gregorian telescopes for millimeter-wave large focal-plane arrays

We compare the diffraction-limited field of view (FOV) provided by four types of off-axis Gregorian telescopes: the classical Gregorian, the aplanatic Gregorian, and the designs that cancel astigmatism and both astigmatism and coma. The analysis is carried out with telescope parameters that are appropriate for satellite and balloonborne millimeter- and submillimeter-wave astrophysics. We find that the design that cancels both coma and astigmatism provides the largest flat FOV, approximately 21 square deg. We also find that the FOV can be increased by approximately 15% by means of optimizing the shape and location of the focal surface.     

Coherent imaging with two-dimensional focal-plane arrays: design and applications

Scanned, single-channel optical heterodyne detection has been used in a variety of lidar applications from ranging and velocity measurements to differential absorption spectroscopy. We describe the design of a coherent camera system that is based on a two-dimensional staring array of heterodyne receivers for coherent imaging applications. Experimental results with a single HgCdTe detector translated in the image plane to form a synthetic two-dimensional array demonstrate the ability to obtain passive heterodyne images of chemical vapor plumes that are invisible to normal video infrared cameras. We describe active heterodyne imaging experiments with use of focal-plane arrays that yield hard-body Doppler lidar images and also demonstrate spatial averaging to reduce speckle effects in static coherent images.