Nonuniformity Correction Techniques Research Articles

Toward snapshot correction of 3D flash LiDAR imagers

We present methods enabling rapid non-uniformity and range walk error correction of 3D flash LiDAR imagers that exhibit electronic crosstalk caused by simultaneously triggering too many detectors. This additional electronic crosstalk is referred to as simultaneous ranging crosstalk noise (SRCN). Using a method in which the 3D flash LiDAR imager views a checkerboard target downrange, the SRCN is largely mitigated. Additionally, processing techniques for computing the non-uniformity correction (NUC) and range walk error correction are described; these include an in-situ thermally compensated dark-frame non-uniformity correction, image processing and filtering techniques for the creation of a photo-response non-uniformity correction, and characterization and correction of the range walk error using data collected across the full focal plane array without the need for sampling or windowing. These methods result in the ability to correct noisy test validation data to a range precision of 8.04 cm and a range accuracy of 1.73 cm and to improve the signal-to-noise ratio of the intensity return by 15 to 49 dB. Visualization of a 3D scene corrected by this process is additionally presented.

A real-time narcissus correction algorithm based on Gaussian mixture models

Cooled infrared optical systems often suffer from narcissus artifacts, which can severely degrade their imaging quality. Although the narcissus effects on image quality can be reduced using appropriate optical designs, the narcissus artifacts cannot be fully eliminated by this approach alone. Other approaches use image processing schemes to treat these artifacts as nonuniform noise and apply nonuniformity correction techniques to eliminate the artifacts. However, as the intensity and form of the narcissus artifacts depend on external factors, such as ambient temperature and focal length of the infrared imaging equipment, target-based nonuniformity correction techniques must frequently be calibrated against a standard blackbody radiation source. Moreover, scene-based nonuniformity corrections often produce ghosting artifacts owing to their overreliance on scene motions. In the present work, narcissus artifacts are treated as a type of additive low-frequency noise. Based on the characteristics of these artifacts and the requirements for real-time performance, we propose a new fitting method and a modified two-dimensional Gaussian mixture model (2D-GMM) for the low-frequency noise associated with narcissus artifacts. This allows the model parameters to be calculated rapidly without an iterative process. The resulting estimates of the narcissus signal can then be directly subtracted from the raw image to eliminate the narcissus artifact. The proposed correction algorithm was experimentally demonstrated to be capable of eliminating narcissus artifacts; furthermore, the fitting and correction processes of the algorithm are sufficiently fast to satisfy the requirements of real-time applications.

Formula omitted] non-uniformity correction of phased-array coils without measuring coil sensitivity

Parallel imaging can be used to increase SNR and shorten acquisition times, albeit, at the cost of image non-uniformity. B1− non-uniformity correction techniques are confounded by signal that varies not only due to coil induced B1− sensitivity variation, but also the object's own intrinsic signal. Herein, we propose a method that makes minimal assumptions and uses only the coil images themselves to produce a single combined B1− non-uniformity-corrected complex image with the highest available SNR. A novel background noise classifier is used to select voxels of sufficient quality to avoid the need for regularization. Unique properties of the magnitude and phase were used to reduce the B1− sensitivity to two joint additive models for estimation of the B1− inhomogeneity. The complementary corruption of the imaged object across the coil images is used to abate individual coil correction imperfections. Results are presented from two anatomical cases: (a) an abdominal image that is challenging in both extreme B1− sensitivity and intrinsic tissue signal variation, and (b) a brain image with moderate B1− sensitivity and intrinsic tissue signal variation. A new relative Signal-to-Noise Ratio (rSNR) quality metric is proposed to evaluate the performance of the proposed method and the RF receiving coil array. The proposed method has been shown to be robust to imaged objects with widely inhomogeneous intrinsic signal, and resilient to poorly performing coil elements.

Quantitative image fusion in infrared radiometry

Towards high-accuracy infrared radiance estimates, measurement practices and processing techniques aimed to achieve quantitative image fusion using a set of multi-exposure images of a static scene are reviewed. The conventional non-uniformity correction technique is extended, as the original is incompatible with quantitative fusion. Recognizing the inherent limitations of even the extended non-uniformity correction, an alternative measurement methodology, which relies on estimates of the detector bias using self-calibration, is developed. Combining data from multi-exposure images, two novel image fusion techniques that ultimately provide high tonal fidelity of a photoquantity are considered: ‘subtract-then-fuse’, which conducts image subtraction in the camera output domain and partially negates the bias frame contribution common to both the dark and scene frames; and ‘fuse-then-subtract’, which reconstructs the bias frame explicitly and conducts image fusion independently for the dark and the scene frames, followed by subtraction in the photoquantity domain. The performances of the different techniques are evaluated for various synthetic and experimental data, identifying the factors contributing to potential degradation of the image quality. The findings reflect the superiority of the ‘fuse-then-subtract’ approach, conducting image fusion via per-pixel nonlinear weighted least squares optimization.

Robust Approach for Nonuniformity Correction in Infrared Focal Plane Array.

In this paper, we propose a new scene-based nonuniformity correction technique for infrared focal plane arrays. Our work is based on the use of two well-known scene-based methods, namely, adaptive and interframe registration-based exploiting pure translation motion model between frames. The two approaches have their benefits and drawbacks, which make them extremely effective in certain conditions and not adapted for others. Following on that, we developed a method robust to various conditions, which may slow or affect the correction process by elaborating a decision criterion that adapts the process to the most effective technique to ensure fast and reliable correction. In addition to that, problems such as bad pixels and ghosting artifacts are also dealt with to enhance the overall quality of the correction. The performance of the proposed technique is investigated and compared to the two state-of-the-art techniques cited above.

Optics Temperature-Dependent Nonuniformity Correction Via <inline-formula> <tex-math notation="LaTeX"> $\ell _{0}$</tex-math> </inline-formula>-Regularized Prior for Airborne Infrared Imaging Systems

In this paper, we propose a new $\ell _{0}$ -regularized approach to remove the temperature-dependent nonuniformity effects induced by the infrared (IR) imaging optics in an aerothermal environment. The $\ell _{0}$ image prior is inspired by observing distinct characteristics of IR images with small targets. Based on this effective prior, we present a variational framework where we optimize an energy functional to estimate the optics-related fixed pattern noise (FPN) and the latent image. A computationally efficient numerical algorithm based on half-quadratic regularization is used to solve the optimization problem. The proposed method is fundamentally different from the existing nonuniformity correction techniques developed for infrared focal plane arrays and simultaneously suppresses the optics-related FPN and random noise. Both quantitative and qualitative comparisons to specialized state-of-art algorithms demonstrate its superiority.

An improved retina-like nonuniformity correction for infrared focal-plane array

The non-uniform response in infrared focal plane array (IRFPA) detectors produces corrupted images with nonuniformity noise. This paper mainly proposes an improved adaptive nonuniformity correction (NUC) method based on the retina-like neural network approach. The main purpose of NUC method is to obtain reliable estimations of gain and offset parameters. In this paper the two correction parameters are updated with two different learning rates respectively for the purpose of updating these two parameters synchronously. And then more accurate estimations of the two correction parameters can be obtained. Again, in order to reduce the ghost artifacts normally introduced by the strong edge effectively, the proposed algorithm employs the non-local means (NLM) method to estimate the desired target value of each detector. The proposed NUC method has been tested by applying it to the IR sequence of frames with simulated nonuniformity noise and real nonuniformity noise, respectively. The performance comparisons are implemented with the well-established scene-based NUC techniques. And the experimental results show the efficiency of the proposed method.

Improving feedback-integrated scene cancellation nonuniformity correction through optimal selection of available camera motion

Scene-based nonuniformity correction (SBNUC) techniques provide a means of identifying and correcting focal plane array nonuniformity (NU) through algorithmic analysis of the camera output. SBNUC techniques rely almost universally on camera motion to provide a means of separating the scene from the NU pattern. A simulation is developed that is used to explore the role and effect of camera motion on two representative registration-based SBNUC algorithms: interframe registration-based least mean square (IRLMS) by Zuo et al. and feedback-integrated scene-cancellation (FiSC) by Black and Tyo. The effect of camera motion velocity and direction between frames is examined. The high spatial frequency portion of NU is shown to be corrected by both IRLMS and FiSC, and this correction is relatively indifferent to nonzero camera motion parameters. The FiSC algorithm was specifically designed to incorporate the low spatial frequency component into registration-based SBNUC, but demonstrates a strong dependency on camera motion. Techniques for mitigation of camera motion parameter effects through tradespace and buffering are presented and tested. With proper mitigation and camera motion, FiSC is shown to correct most high and low spatial frequency NU with fewer than 100 framepairs repeatedly processed using techniques suitable for real-time processing implementation.

The Need of Terahertz Cameras for Standardizing Sensitivity Measurements

The applicability of a blackbody source to sensitivity measurements and calibration of an uncooled Terahertz (THz) focal plane array (FPA) is discussed, but the combination of a blackbody source and a band-pass filter may not be suitable for these purposes. Two ways to measure the minimum detectable power (MDP) of uncooled THz-FPAs are described and compared when used with strong THz sources such as a quantum cascade laser (QCL). The MDP is defined as the radiant power that produces a signal-to-noise ratio of unity in the output of a THz imager that uses a THz-FPA operating at a TV frame rate. One method (method 1) is based on the hypothesis that the beam pattern of the THz source is close to a Gaussian pattern. In another method (method 2), the signal level of the background in an image that does not contain a THz source is subtracted from the signal of the image. While method 2 is more flexible, how large the signals coming from THz source should be, as compared with the noise level, remains to be defined. Finally, based on issues with the current non-uniformity correction (NUC) technique, specifications are proposed for THz source power and wobbling technique to obtain uniform illumination for an ideal NUC technique.

Feedback-integrated scene cancellation scene-based nonuniformity correction algorithm

A new registration-based scene-based nonuniformity correction (SBNUC) technique called the feedback-integrated scene cancellation (FiSC) method is introduced, which demonstrates an ability to correct both high- and low-spatial frequency nonuniformity (NU) in infrared focal plane arrays. The theory of scene-cancellation is further developed to include a referencing mechanism that allows spatially correlated NU to be corrected, and a practical method of application is developed. The algorithm is suitable for implementation in a real-time processing environment such as a digital signal processor. A new metric called normalized root mean-squared error for quantifying SBNUC performance is introduced and applied. When applied to real data from a cooled HgTeCd focal plane, the FiSC algorithm outperforms other SBNUC algorithms considered when provided with accurate frame-to-frame image registration. An SBNUC simulation is described and applied to several SBNUC algorithms. When the most realistic case including both high- and low-spatial frequency NU is simulated, the FiSC algorithm outperforms all others tested.

Single-image-based solution for optics temperature-dependent nonuniformity correction in an uncooled long-wave infrared camera

In this Letter, we propose an efficient and accurate solution to remove temperature-dependent nonuniformity effects introduced by the imaging optics. This single-image-based approach computes optics-related fixed pattern noise (FPN) by fitting the derivatives of correction model to the gradient components, locally computed on an infrared image. A modified bilateral filtering algorithm is applied to local pixel output variations, so that the refined gradients are most likely caused by the nonuniformity associated with optics. The estimated bias field is subtracted from the raw infrared imagery to compensate the intensity variations caused by optics. The proposed method is fundamentally different from the existing nonuniformity correction (NUC) techniques developed for focal plane arrays (FPAs) and provides an essential image processing functionality to achieve completely shutterless NUC for uncooled long-wave infrared (LWIR) imaging systems.

Scene-based Nonuniformity Correction Algorithm Based on Temporal Median Filter

Scene-based nonuniformity correction techniques for infrared focal-plane arrays have been widely considered as a key technology, and various algorithms have been proposed to compensate for fixed-pattern noise. However, the existed algorithms' capability is always restricted by the problems of convergence speed and ghosting artifacts. In this paper, an effective scene-based nonuniformity correction method is proposed to solve these problems. The algorithm is an improvement over the constant statistics method and a temporal median is utilized with the Gaussian kernel to estimate the nonuniformity parameters. Also theoretical analysis is conducted to demonstrate that effective ghosting artifacts elimination and superior convergence speed can be obtained with the proposed method. Finally, the performance of the proposed technique is tested with infrared image sequences with simulated nonuniformity and with infrared imagery with real nonuniformity. The results show the proposed method is able to estimate each detector's gain and to offset reliably and that it performs better in increasing convergence speed and reducing ghosting artifacts compared with the conventional techniques.

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.

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.

Bilateral filter-based adaptive nonuniformity correction for infrared focal-plane array systems

In scene-based nonuniformity correction (NUC) methods for infrared focal-plane array cameras, the problem of ghosting artifacts widely affects the sensitivity of the imaging system and visibly decreases the image quality. Ghosting artifacts can also degrade the performance of several applications, such as target detection and tracking. We carried out a detailed analysis of the problem using a well-established NUC technique: the least mean square Scribner's algorithm. In order to solve some drawbacks of the original Scribner's algorithm, we introduced in the scheme a new technique that mitigates ghosting. Such technique relies on the employment of an edge-preserving spatial filter for the purpose of computing reliable spatial estimates. We tested the effectiveness of the new technique applying the improved NUC method to an experimental IR sequence of frames acquired in the laboratory. Finally, the performance of the proposed method was discussed and compared to that yielded by a well-established deghosting technique.

Ghosting reduction in scene-based nonuniformity correction of infrared image sequences

Scene-based adaptive nonuniformity correction (NUC) is currently being applied to achieve higher performance in infrared imaging systems. However, almost all scene-based NUC algorithms cause the production of ghosting artifacts over output images. Based on constant-statistics theory, we propose a novel threshold self-adaptive ghosting reduction algorithm to improve the space low-pass and temporal high-pass (SLPTHP) NUC technique. The correction parameters of the previous frame are regarded as thresholds to compute new correction parameters. Experimental results show that the proposed algorithm can obtain a satisfactory performance in reducing unwanted ghosting artifacts.

Temporal statistics de-ghosting for adaptive non-uniformity correction in infrared focal plane arrays

The LMS-based adaptive non-uniformity correction (NUC) technique, known in the literature as Scribner's algorithm, is an efficient method to mitigate the presence of fixed pattern noise in video sequences acquired by thermal cameras. Unfortunately, ghosting artefacts can be generated from the process of NUC depending on the edges that characterise the scene inside the sensor's field of view. Introduced is a de-ghosting technique based on the computation of the statistics of the error signal employed in the Scribner's algorithm. Such a technique is characterised by a small computational load which is the most important requirement for real-time applications. Experimental results demonstrate its good performance and de-ghosting capabilities.

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.

Scene-based nonuniformity correction technique for infrared focal-plane arrays

A scene-based nonuniformity correction algorithm is presented to compensate for the gain and bias nonuniformity in infrared focal-plane array sensors, which can be separated into three parts. First, an interframe-prediction method is used to estimate the true scene, since nonuniformity correction is a typical blind-estimation problem and both scene values and detector parameters are unavailable. Second, the estimated scene, along with its corresponding observed data obtained by detectors, is employed to update the gain and the bias by means of a line-fitting technique. Finally, with these nonuniformity parameters, the compensated output of each detector is obtained by computing a very simple formula. 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 every module is demonstrated with simulated and real infrared image sequences. Experimental results indicate that the proposed algorithm exhibits a superior correction effect.

A MAP Estimator for Simultaneous Superresolution and Detector Nonunifomity Correction

During digital video acquisition, imagery may be degraded by a number of phenomena including undersampling, blur, and noise. Many systems, particularly those containing infrared focal plane array (FPA) sensors, are also subject to detector nonuniformity. Nonuniformity, or fixed pattern noise, results from nonuniform responsivity of the photodetectors that make up the FPA. Here we propose a maximuma posteriori (MAP) estimation framework for simultaneously addressing undersampling, linear blur, additive noise, and bias nonuniformity. In particular, we jointly estimate a superresolution (SR) image and detector bias nonuniformity parameters from a sequence of observed frames. This algorithm can be applied to video in a variety of ways including using a moving temporal window of frames to process successive groups of frames. By combining SR and nonuniformity correction (NUC) in this fashion, we demonstrate that superior results are possible compared with the more conventional approach of performing scene-based NUC followed by independent SR. The proposed MAP algorithm can be applied with or without SR, depending on the application and computational resources available. Even without SR, we believe that the proposed algorithm represents a novel and promising scene-based NUC technique. We present a number of experimental results to demonstrate the efficacy of the proposed algorithm. These include simulated imagery for quantitative analysis and real infrared video for qualitative analysis.