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.     

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.     

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.     

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.     

Two-color imaging by the use of patterned optical filters bonded to focal-plane-array detectors

Two-color imaging by the use of a patterned optical filter directly bonded to a HgCdTe focal-plane array is demonstrated. The optical filter is spatially patterned, by multilayer lithographic techniques, to provide multicolor response in the midwavelength infrared by the use of a single substrate.     

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.     

Noise-cancellation-based nonuniformity correction algorithm for infrared focal-plane arrays

The spatial fixed-pattern noise (FPN) inherently generated in infrared (IR) imaging systems compromises severely the quality of the acquired imagery, even making such images inappropriate for some applications. The FPN refers to the inability of the photodetectors in the focal-plane array to render a uniform output image when a uniform-intensity scene is being imaged. We present a noise-cancellation-based algorithm that compensates for the additive component of the FPN. The proposed method relies on the assumption that a source of noise correlated to the additive FPN is available to the IR camera. An important feature of the algorithm is that all the calculations are reduced to a simple equation, which allows for the bias compensation of the raw imagery. The algorithm performance is tested using real IR image sequences and is compared to some classical methodologies.     

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.     

PdSi focal-plane array detectors for short-wave infrared Raman spectroscopy of biological tissue: a feasibility study

We have used a PdSi focal-plane array detector to measure short-wave infrared Raman spectra of pure compounds and human tissue. Raman bands of the pure compounds are clearly visible in the spectra, and a calcification feature at 960 cm(-1) is readily identifiable in the spectra of diseased human aorta. The performance characteristics of our detection device were good; dark noise contributed approximately 60 (electrons/s)/pixel, and the read noise was ~50 rms electrons/pixel. The primary noise in the spectra was due to fixed-pattern noise, which is the variation in measured signal across a detector when it is uniformly illuminated.     

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.     

基于维纳滤波的红外焦平面非均匀性校正算法

针对焦平面阵列上各探测单元光电响应的非均匀性,本文使用了维纳滤波技术来实现红外焦平面阵列非均匀校正.该方法首先根据实际情况确定一个输出延迟,然后采用维纳滤波并借助前后帧信息对当前帧进行多次估计,最后取其均值作为此帧的最终校正结果.文中使用了真实红外图像对算法性能进行验证,由于能够充分利用过去和将来的场景信息,因而本算法可以有效地去除原图像上的固定图案噪声.     

Variational Bayesian Based IMM Robust GPS Navigation Filter

This paper investigates the navigational performance of Global Positioning System (GPS) using the variational Bayesian (VB) based robust filter with interacting multiple model (IMM) adaptation as the navigation processor. The performance of the state estimation for GPS navigation processing using the family of Kalman filter (KF) may be degraded due to the fact that in practical situations the statistics of measurement noise might change. In the proposed algorithm, the adaptivity is achieved by estimating the time-varying noise covariance matrices based on VB learning using the probabilistic approach, where in each update step, both the system state and time-varying measurement noise were recognized as random variables to be estimated. The estimation is iterated recursively at each time to approximate the real joint posterior distribution of state using the VB learning. One of the two major classical adaptive Kalman filter (AKF) approaches that have been proposed for tuning the noise covariance matrices is the multiple model adaptive estimate (MMAE). The IMM algorithm uses two or more filters to process in parallel, where each filter corresponds to a different dynamic or measurement model. The robust Huber's M-estimation-based extended Kalman filter (HEKF) algorithm integrates both merits of the Huber M-estimation methodology and EKF. The robustness is enhanced by modifying the filter update based on Huber's M-estimation method in the filtering framework. The proposed algorithm, referred to as the interactive multi-model based variational Bayesian HEKF (IMM-VBHEKF), provides an effective way for effectively handling the errors with time-varying and outlying property of non-Gaussian interference errors, such as the multipath effect. Illustrative examples are given to demonstrate the navigation performance enhancement in terms of adaptivity and robustness at the expense of acceptable additional execution time.     

Focal-plane processing architectures for real-time hyperspectral image processing

Real-time image processing requires high computational and I/O throughputs obtained by use of optoelectronic system solutions. A novel architecture that uses focal-plane optoelectronic-area I/O with a fine-grain, low-memory, single-instruction-multiple-data (SIMD) processor array is presented as an efficient computational solution for real-time hyperspectral image processing. The architecture is evaluated by use of realistic workloads to determine data throughputs, processing demands, and storage requirements. We show that traditional store-and-process system performance is inadequate for this application domain, whereas the focal-plane SIMD architecture is capable of supporting real-time performances with sustained operation throughputs of 500-1500 gigaoperations/s. The focal-plane architecture exploits the direct coupling between sensor and parallel-processor arrays to alleviate data-bandwidth requirements, allowing computation to be performed in a stream-parallel computation model, while data arrive from the sensors.     

SeaWiFS on-orbit gain and detector calibrations: effect on ocean products

The NASA Ocean Biology Processing Group's Calibration and Validation Team has analyzed the mission-long Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) on-orbit gain and detector calibration time series to verify that lunar calibrations, obtained at nonstandard gains and radiance ranges, are valid for Earth data collected at standard gains and typical ocean, cloud, and land radiances. For gain calibrations, a constant voltage injected into the postdetector electronics allows gain ratios to be computed for all four detectors in each band. The on-orbit lunar gain ratio time series show small drifts for the near infrared bands. These drifts are propagated into the ocean color data through the atmospheric correction parameter epsilon, which uses the 765/865 nm band ratio. An anomaly analysis of global mean normalized water-leaving radiances at 510 nm shows a small decrease over the mission, while an analysis of epsilon shows a corresponding increase. The drifts in the lunar time series for the 765 and 865 nm bands were corrected. An analysis of the revised water-leaving radiances at 510 nm shows the drift has been eliminated, while an analysis of epsilon shows a reduced drift. For detector calibrations, solar diffuser observations made by the individual detectors in each band allows the response of the detectors to be monitored separately. The mission-long time series of detector calibration data show that the variations in the response of the individual detectors are less than 0.5% over the mission for all bands except the 865 nm band, where the variations are less than 1%.     

Comparison of quarter-wave retarders over finite spectral and angular bandwidths for infrared polarimetric-imaging applications

We compare three technological approaches for quarter-wave retarders within the context of polarimetric-imaging applications in the long-wave infrared (LWIR) spectrum. Performance of a commercial cadmium sulfide (CdS) crystalline waveplate, a multilayer meanderline structure, and a silicon (Si) form-birefringent retarder are evaluated under conditions of 8-12 μm broadband radiation emerging from an F/1 focusing objective. Metrics used for this comparison are the spectrally dependent axial ratio, retardance, and polarization-averaged power transmittance, which are averaged over the angular range of interest. These parameters correspond to the characteristics that would be observed at the focal-plane array (FPA) detector of an LWIR imaging polarimeter.     

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.     

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.     

Focal-plane pixel-energy redistribution and concentration by use of microlens arrays

A physical-optics calculation was performed to study the effects of a microlens array placed over a focal-plane detector array. In certain conditions the light is further concentrated to a spot size that is smaller than the point-spread function of the receiver optics. It is also shown that the microlens refocuses a sinc-squared point-spread function to a shape that is more uniform as well as narrower. Numerical examples were made for the far IR.     

Barker-coded ultrasound color flow imaging: theoretical and practical design considerations

Barker-coded excitation is applied to improve the sensitivity versus resolution tradeoff for ultrasound color flow imaging (CFI). Direct sequence encoding and complex baseband decoding methods that enable flexible combination of demodulation frequency and Barker-chip duration are proposed. Based on a general Wiener decoding filter formulation, three different conditions that pertain to the relationship between the Barker-chip duration and demodulation frequency are found, such that they result in real, complex symmetric and complex asymmetric decoding filter sequences, respectively. It is also shown that the matched filter and the inverse filter represent two extreme cases of the Wiener filter, and the latter is proposed for decoding Barker-coded CFI signals with maximum range sidelobe suppression. Some practical considerations such as coding gain, integrated sidelobe level (ISL) and peak sidelobe level (PSL) for various decoding filter lengths, and the influence of flow rate also are analyzed. Linear array imaging of steady flow in straight tubes is simulated based on a 4-cycle base pulse at 6.25 MHz, a 5-chip Barker code, a 32-tap decoding filter, and standard color flow data processing. The resultant color flow images demonstrate the expected improvements in penetration and axial resolution.