MODELING, PROCESSING AND EVALUATION TECHNIQUES FOR ADVANCED INFRARED IMAGING SYSTEMS

Abstract

Infrared imaging is an emerging technology that has evolved in the last forty years. Infrared imaging systems have become ideal for military forces since World War-II. Infrared technology has totally changed the nature of modern warfare, including homeland security. It allows the operator to see and acquire targets in total darkness, including adverse weather conditions (poor ambient light). Infrared imaging systems offer several advantages over other classes of night vision devices based on image intensification. In the first place, it can be used in complete darkness, while image intensifiers require some form of ambient light, be it only starlight. Infrared imaging systems can detect objects at considerably longer distances than image intensification based systems. In view of the advantages mentioned above, infrared imaging systems are extensively used in defence for every platform, from airborne to small weapon systems. The development of infrared imaging systems based on high performance advanced infrared detectors requires an effective range model to estimate the range performance. Integrated processing modules such as non-uniformity correction and digital video stabilization are required for effective engagement of target from dynamic platforms. Performance evaluation of these imaging systems is a tedious and complex task. Thus, the development of new and high-performance imaging systems calls for the incorporation of an upgrade atmospheric and detector models, efficient processing algorithms, and objective evaluation techniques for improved target detection and engagement. The aim of this research work is to comprehensively explore the techniques of range modeling, processing, and performance evaluation for advanced infrared imaging systems. There are four main objectives of the thesis. a) Modeling to predict the range performance b) Processing of data for non-uniformity correction and digital video stabilization c) Objective performance evaluation d) Validation of range performance in field conditions This thesis primarily focuses on the modeling, processing, and evaluation techniques for advanced infrared imaging systems. The thesis consists of seven chapters. Each chapter begins with a brief introduction to the concerned problem and its possible solution. The experimental results are summarized and concluded at the end of each chapter. This thesis begins with an introduction to infrared imaging technology, design challenges, and the motivation of taking up the specific problem in Chapter 1. Chapter 2 presents an extensive literature review on the latest development in IR detector technology along with the prediction model, suitable processing, and evaluation techniques for advanced IR imaging systems. Chapter 3 describes a comparative study of acquisition range performance of infrared imaging system operating in LWIR and MWIR spectral bands as a function of a single parameter, i.e., absolute humidity. Advancements x in infrared sensor technology necessitate an efficient processing module to improve the performance of infrared imaging systems further. Chapter 4 presents an effective non-uniformity correction algorithm and its real-time hardware implementation in reconfigurable architecture to improve the non-uniformity significantly. The proposed algorithm is able to reduce non-uniformities from 12% to less than 1%. Chapter 5 presents an algorithm for digital video stabilization with smear removal. The algorithm is developed under the framework of speeded-up robust features matching for video stabilization. The proposed algorithm is capable of correcting both translation and rotational motions. It is demonstrated that an Inter-frame Transformation Fidelity (ITF) gain of 6-21 dB is achieved for sample videos under consideration. An accurate, objective evaluation of infrared imaging systems is presented in Chapter 6. The proposed objective minimum resolvable temperature difference (MRTD) measurement method is compared to the conventional method of evaluation, i.e., subjective MRTD. This objective MRTD measurement method provides a quick quantitative evaluation with a percentage error of less than 5% compared to the subjective evaluation. Chapter 7 concludes the research work, with conclusions based on the results obtained and major outcomes. Future scope of the work for further improvement is also deliberated in conclusion.