Abstract
Unmanned aerial vehicles (UAVs) are equipped with optical systems including an infrared (IR) camera such as electro-optical IR (EO/IR), target acquisition and designation sights (TADS), or forward looking IR (FLIR). However, images obtained from IR cameras are subject to noise such as dead pixels, lines, and fixed pattern noise. Nonuniformity correction (NUC) is a widely employed method to reduce noise in IR images, but it has limitations in removing noise that occurs during operation. Methods have been proposed to overcome the limitations of the NUC method, such as two-point correction (TPC) and scene-based NUC (SBNUC). However, these methods still suffer from unfixed pattern noise. In this paper, a background registration-based adaptive noise filtering (BRANF) method is proposed to overcome the limitations of conventional methods. The proposed BRANF method utilizes background registration processing and robust principle component analysis (RPCA). In addition, image quality verification methods are proposed that can measure the noise filtering performance quantitatively without ground truth images. Experiments were performed for performance verification with middle wave infrared (MWIR) and long wave infrared (LWIR) images obtained from practical military optical systems. As a result, it is found that the image quality improvement rate of BRANF is 30% higher than that of conventional NUC.
Highlights
Electro-optical infrared (EO/IR), target acquisition and designation sights (TADS), and forward looking infrared (FLIR) sensors are mounted on weapons systems such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), combat planes, and helicopters
Is the most popular popular methodan applied in practical military systems, and Weighted nuclear nuclear minimization (WNNM) is the result of one of the method applied in practical military optical systems, and is the result of one of the
BRANF was proposed to overcome the limits of conventional methods
Summary
Electro-optical infrared (EO/IR), target acquisition and designation sights (TADS), and forward looking infrared (FLIR) sensors are mounted on weapons systems such as unmanned aerial vehicles (UAVs), unmanned ground vehicles (UGVs), combat planes, and helicopters. Weapons systems acquire a tactical map of the battlefield or critical data for tracking and attacking targets using electro-optical sensors. Electro-optical sensor systems used in weapon systems are required to provide continuous high-quality images in order to detect the target even in low-visibility and foggy conditions. IR cameras are used in addition to daylight cameras, to improve the visibility for detecting and tracking targets in severe environments [1]. Additional noise is generated in IR images when such cameras are employed in severe environments, such as battlefields. Such noise significantly degrades the performance of target detection and tracking
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