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

Multi-Model Kalman Filtering for Adaptive Nonuniformity: Correction in Infrared Sensors

This paper presents 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 dynamical-model parameters, which underly the statistics of the scene and the nonuniformity as well as the temporal drift in the nonuniformity. 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 obtained according to the a posteriori -likelihood principle, applied to the family of models by considering the output residual errors associated with each filter. These weights are updated iteratively between blocks of data, providing the estimator the means to follow the dynamics of the scenes and the sensor. The performance of the proposed estimator and its ability to compensate for fixed-pattern noise are tested using both real and simulated data. The real data is obtained using two cameras operating in the mid- and long-wave infrared regime

Generalized Algebraic Algorithm for Scene-based Nonuniformity Correction

ABSTRACT This paper presents an overview of three recently developed scene-based nonuniformity correction techniques, namely, the algebraic scene-based algorithm (ASBA), the extended radiometrically accurate scene-based algorithm (RASBA) and the generalized algebraic scene-based algorithm (GASBA). The ASBA uses pairs of image frames that exhibit one-dimension sub-pixel motion to algebraically extract estimates of bias nonuniformity. The RASBA incorporates arbitrary sub-and super-pixel two-dimensional motion in conjunction with limited perimeter-only absolute calibration to obtain radiometrically accurate estimates of the bias nonuniformity. The RASBA provides the advantage of being able to maintain radiometry in the interior photodetectors without interrupting their operation. The GASBA is a generalized non-radiometric form of the algorithm that uses image pairs with arbitrary two-dimensional motion and encompasses both the ASBA and RASBA algorithms. This generalization is accomplished by initially guaranteeing bias uniformity in the perimeter detectors. This uniformity can be achieved by first applying the ASBA estimates. The generalized algorithm is then able to automatically maintain perimeter uniformity without the need for re-application of the ASBA. Thus, the GASBA is able to operate completely in a non-radiometric mode, alleviating the need for the perimeter calibration system if desired. The generalized algorithm is applied to real infrared imagery obtained from both cooled and uncooled infrared cameras. A hardware implementation of the proposed algorithm will also be discussed along with several ongoing commercial applications of the technology

Digital Image Processing

In recent years, digital images and digital image processing have become part of everyday life. This growth has been primarily fueled by advances in digital computers and the advent and growth of the Internet. Furthermore, commercially available digital cameras, scanners, and other equipment for acquiring, storing, and displaying digital imagery have become very inexpensive and increasingly powerful. An excellent treatment of digital images and digital image processing can be found in Ref. [1]. A digital image is simply a two-dimensional array of finite-precision numerical values called picture elements (or pixels). Thus a digital image is a spatially discrete (or discrete-space) signal. In visible grayscale images, for example, each pixel represents the intensity of a corresponding region in the scene. The grayscale values must be quantized into a finite precision format. Typical resolutions include 8 bit (256 gray levels), 12 bit (4096 gray levels), and 16 bit (65536 gray levels). Color visible images are most frequently represented by tristimulus values. These are the quantities of red, green, and blue light required, in the additive color system, to produce the desired color. Thus a so-called “RGB” color image can be thought of as a set of three “grayscale” images — the first representing the red component, the second the green, and the third the blue. Digital images can also be nonvisible in nature. This means that the physical quantity represented by the pixel values is something other than visible light intensity or color. These include radar cross-sections of an object, temperature profile (infrared imaging), X-ray images, gravitation field, etc. In general, any two-dimensional array information can be the basis for a digital image. As in the case of any digital data, the advantage of this representation is in the ability to manipulate the pixel values using a digital computer or digital hardware. This offers great power and flexibility. Furthermore, digital images can be stored and transmitted far more reliably than their analog counterparts. Error protection coding of digital imagery, for example, allows for virtually error-free transmission

Fixed pattern noise compensation in a mercury cadmium telluride infrared focal plane array

Bibliography: pages 106-109.This thesis describes techniques for the correction of spatial noise artifacts in a mercury cadmium telluride infrared camera system. The spatial noise artifacts are a result of nonuniformities within the infrared focal plane detector array. The techniques presented dispense with the need for traditional temperature references, and provide nonuniformity compensation by using only the statistics of the moving infrared scene and motion of the camera assembly for calibration. Frame averaging is employed, assuming that all of the detector pixels will eventually be irradiated with the same levels of incident flux after some extended period of time. Using a statistical analysis of the camera image data, the correction coefficients are re-calculated and updated. These techniques also ensure that the calculated coefficients continually track the variations in the dark currents as well as temperature changes within the dewar sensor cooling vessel. These scene-based reference free approaches to the calculation of compensation coefficients in the infrared camera are shown to be successful in compensating for the effects of fixed pattern spatial noise

Combined Industry, Space and Earth Science Data Compression Workshop

The sixth annual Space and Earth Science Data Compression Workshop and the third annual Data Compression Industry Workshop were held as a single combined workshop. The workshop was held April 4, 1996 in Snowbird, Utah in conjunction with the 1996 IEEE Data Compression Conference, which was held at the same location March 31 - April 3, 1996. The Space and Earth Science Data Compression sessions seek to explore opportunities for data compression to enhance the collection, analysis, and retrieval of space and earth science data. Of particular interest is data compression research that is integrated into, or has the potential to be integrated into, a particular space or earth science data information system. Preference is given to data compression research that takes into account the scien- tist's data requirements, and the constraints imposed by the data collection, transmission, distribution and archival systems

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided