Preliminaries

This chapter introduces some common operations in infrared image processing, mainly including the following four points: (1) Morphological Transformation: Morphological image processing refers to a series of image processing techniques that deal with the shape characteristics of images. The basic idea of morphology is to use a special structural element to measure or extract the corresponding shape or feature in the input image for further image analysis and target recognition. (2) PCA: Principal component analysis (PCA) is a statistical method. A set of potentially correlated variables is transformed into a set of linearly uncorrelated variables through orthogonal transformation, and the transformed set of variables is called principal components. In practical projects, in order to analyze the problem comprehensively, many variables (or factors) related to this are often put forward because each variable reflects some information of the project to varying degrees. Principal component analysis was first introduced by K. Pearson (Karl Pearson) for non-random variables, and then H. Hotelling extended this method to the case of random vectors. The size of the information is usually measured by the sum of squared deviations or variance. (3) ADMM: The Alternating Direction Method of Multipliers is usually used to solve optimization problems with two optimization variables that contain only equality constraints and is widely used in signal processing, image processing, machine learning, engineering calculations, etc. In the field, it has the advantages of fast convergence speed and good convergence performance. (4) Evaluation Metrics: This chapter introduces objective evaluation indicators commonly used in infrared small target detection algorithms, including background suppression factor (BSF), signal-to-clutter ratio gain (SCRG), probability of detection (Pd), false alarm rate (Fa), and area under the curve (AUC).