Bradley M. Ratliff

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.

Total elimination of sampling errors in polarization imagery obtained with integrated microgrid polarimeters.

Microgrid polarimeters operate by integrating a focal plane array with an array of micropolarizers. The Stokes parameters are estimated by comparing polarization measurements from pixels in a neighborhood around the point of interest. The main drawback is that the measurements used to estimate the Stokes vector are made at different locations, leading to a false polarization signature owing to instantaneous field-of-view (IFOV) errors. We demonstrate for the first time, to our knowledge, that spatially band limited polarization images can be ideally reconstructed with no IFOV error by using a linear system framework.

Interpolation strategies for reducing IFOV artifacts in microgrid polarimeter imagery

Microgrid polarimeters are composed of an array of micro-polarizing elements overlaid upon an FPA sensor. In the past decade systems have been designed and built in all regions of the optical spectrum. These systems have rugged, compact designs and the ability to obtain a complete set of polarimetric measurements during a single image capture. However, these systems acquire the polarization measurements through spatial modulation and each measurement has a varying instantaneous field-of-view (IFOV). When these measurements are combined to estimate the polarization images, strong edge artifacts are present that severely degrade the estimated polarization imagery. These artifacts can be reduced when interpolation strategies are first applied to the intensity data prior to Stokes vector estimation. Here we formally study IFOV error and the performance of several bilinear interpolation strategies used for reducing it.

The effects of thermal equilibrium and contrast in LWIR polarimetric images

Long-wave infrared (LWIR) polarimetric signatures provide the potential for day-night detection and identification of objects in remotely sensed imagery. The source of optical energy in the LWIR is usually due to thermal emission from the object in question, which makes the signature dependent primarily on the target and not on the external environment. In this paper we explore the impact of thermal equilibrium and the temperature of (unseen) background objects on LWIR polarimetric signatures. We demonstrate that an object can completely lose its polarization signature when it is in thermal equilibrium with its optical background, even if it has thermal contrast with the objects that appear behind it in the image.

Unpolarized calibration and nonuniformity correction for long-wave infrared microgrid imaging polarimeters

Recent developments for long-wave infrared (LWIR) imaging polarimeters include incorporating a microgrid polarizer array onto the focal plane array. Inherent advantages over other classes of polarimeters include rugged packaging, inherent alignment of the optomechanical system, and temporal synchronization that facilitates instantaneous acquisition of both thermal and polarimetric information. On the other hand, the pixel-to-pixel instantaneous field-of-view error that is inherent in the microgrid strategy leads to false polarization signatures. Because of this error, residual pixel-to-pixel variations in the gain-corrected responsivity, the noise-equivalent input, and variations in the pixel-to-pixel micropolarizer performance are extremely important. The degree of linear polarization is highly sensitive to these parameters and is consequently used as a metric to explore instrument sensitivities. We explore the unpolarized calibration issues associated with this class of LWIR polarimeters and discuss the resulting false polarization signature for thermally flat test scenes.

Image processing methods to compensate for IFOV errors in microgrid imaging polarimeters

Long-wave infrared imaging Stokes vector polarimeters are used in many remote sensing applications. Imaging polarimeters require that several measurements be made under optically different conditions in order to estimate the polarization signature at a given scene point. This multiple-measurement requirement introduces error in the signature estimates, and the errors differ depending upon the type of measurement scheme used. Here, we investigate a LWIR linear microgrid polarimeter. This type of instrument consists of a mosaic of micropolarizers at different orientations that are masked directly onto a focal plane array sensor. In this scheme, each polarization measurement is acquired spatially and hence each is made at a different point in the scene. This is a significant source of error, as it violates the requirement that each polarization measurement have the same instantaneous field-of-view (IFOV). In this paper, we first study the amount of error introduced by the IFOV handicap in microgrid instruments. We then proceed to investigate means for mitigating the effects of these errors to improve the quality of polarimetric imagery. In particular, we examine different interpolation schemes and gauge their performance. These studies are completed through the use of both real instrumental and modeled data.

Super-Resolution for Imagery from Integrated Microgrid Polarimeters

Imagery from microgrid polarimeters is obtained by using a mosaic of pixel-wise micropolarizers on a focal plane array (FPA). Each distinct polarization image is obtained by subsampling the full FPA image. Thus, the effective pixel pitch for each polarization channel is increased and the sampling frequency is decreased. As a result, aliasing artifacts from such undersampling can corrupt the true polarization content of the scene. Here we present the first multi-channel multi-frame super-resolution (SR) algorithms designed specifically for the problem of image restoration in microgrid polarization imagers. These SR algorithms can be used to address aliasing and other degradations, without sacrificing field of view or compromising optical resolution with an anti-aliasing filter. The new SR methods are designed to exploit correlation between the polarimetric channels. One of the new SR algorithms uses a form of regularized least squares and has an iterative solution. The other is based on the faster adaptive Wiener filter SR method. We demonstrate that the new multi-channel SR algorithms are capable of providing significant enhancement of polarimetric imagery and that they outperform their independent channel counterparts.

Algebraic scene-based nonuniformity correction in focal-plane arrays

An algorithm is developed to compensate for the spatial fixed-pattern (nonuniformity) noise in focal-plane arrays, which is a pressing problem, particularly for mid- to far- infrared imaging systems. The proposed algorithm uses pairs of frames from an image sequence exhibiting pure horizontal and vertical sub-pixel shifts. The algorithm assumes a linear irradiance-voltage model for which the nonuniformity is attributed only to variation in the offset of various detectors in the array. Using a modified gradient-based shift estimator, pairs of frames exhibiting the above shift requirements can be identified and used to generate a correction matrix, which will compensate for the offset nonuniformity in all frames. The efficacy of this nonuniformity correction technique is demonstrated by applying it to infrared and simulated data. The strength of this technique is in its simplicity, requiring relatively few frames to generate an acceptable correction matrix.

An algebraic restoration method for estimating fixed-pattern noise in infrared imagery from a video sequence

The inherent nonuniformity in the photoresponse and readout-circuitry of the individual detectors in infrared focal-plane-array imagers result in the notorious fixed-pattern noise (FPN). FPN generally degrades the performance of infrared imagers and it is particularly problematic in the midwavelength and longwavelength infrared regimes. In many applications, employing signal-processing techniques to combat FPN may be preferred over hard calibration (e.g., two-point calibration), as they are less expensive and, more importantly, do not require halting the operation of the camera. In this paper, a new technique that uses knowledge of global motion in a video sequence to restore the true scene in the presence of FPN is introduced. In the proposed setting, the entire video sequence is regarded as an output of a motion-dependent linear transformation, which acts collectively on the true scene and the unknown bias elements (which represent the FPN) in each detector. The true scene is then estimated from the video sequence according to a minimum mean-square-error criterion. Two modes of operation are considered. First, we consider non-radiometric restoration, in which case the true scene is estimated by performing a regularized minimization, since the problem is ill-posed. The other mode of operation is radiometric, in which case we assume that only the perimeter detectors have been calibrated. This latter mode does not require regularization and therefore avoids compromising the radiometric accuracy of the restored scene. The algorithm is demonstrated through preliminary results from simulated and real infrared imagery.

Evaluation and display of polarimetric image data using long-wave cooled microgrid focal plane arrays

Recent developments for Long Wave InfraRed (LWIR) imaging polarimeters include incorporating a microgrid polarizer array onto the focal plane array (FPA). Inherent advantages over typical polarimeters include packaging and instantaneous acquisition of thermal and polarimetric information. This allows for real time video of thermal and polarimetric products. The microgrid approach has inherent polarization measurement error due to the spatial sampling of a non-uniform scene, residual pixel to pixel variations in the gain corrected responsivity and in the noise equivalent input (NEI), and variations in the pixel to pixel micro-polarizer performance. The Degree of Linear Polarization (DoLP) is highly sensitive to these parameters and is consequently used as a metric to explore instrument sensitivities. Image processing and fusion techniques are used to take advantage of the inherent thermal and polarimetric sensing capability of this FPA, providing additional scene information in real time. Optimal operating conditions are employed to improve FPA uniformity and sensitivity. Data from two DRS Infrared Technologies, L.P. (DRS) microgrid polarizer HgCdTe FPAs are presented. One FPA resides in a liquid nitrogen (LN2) pour filled dewar with a 80°K nominal operating temperature. The other FPA resides in a cryogenic (cryo) dewar with a 60° K nominal operating temperature.