Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications

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.

Residual Forward-Subtracted U-Shaped Network for Dynamic and Static Image Restoration

“…Reflecting this, ETH Zurich's computer vision laboratory in Switzerland has been leading the field of image restoration by holding the NTIRE (New Trends in Image Restoration and Enhancement workshop 2 ) challenge every year since 2016. 2 [Online]. Available: http://www.vision.ee.ethz.ch/ntire19/ In the restoration task proposed in this paper, practicality is considered for application in various applications.…”

“…In particular, techniques used in application systems such as object detection, tracking, and segmentation can show various performance limitations when using deteriorated images. The background registration-based adaptive noise filtering (BRANF) algorithm has been studied to solve both static and dynamic noise issues, And also general machine learning-based algorithms and the cross fusion-based adaptive contrast (CFACE) have been studied to solve these problems, but they show limitations in effectively removing noise with high uniformity and speed [2], [3], [4], [5].…”

Cross Fusion-Based Low Dynamic and Saturated Image Enhancement for Infrared Search and Tracking Systems

“…Infrared search and tracking (IRST) systems are used on unmanned aerial vehicles (UAV) and surveillance applications such as pan-tilt searching electro-optical systems [1]. In such applications, it is required to process contrast expansion and noise reduction because of the low output characteristics of IR detection sensors and because its image dynamic range is severely degraded by low visibility conditions occurring due to light scattering by microscopic aerosols such as the fog and haze.…”

Weighted Kernel Filter Based Anti-Air Object Tracking for Thermal Infrared Systems

“…On the other hand, TIR is more commonly used to observe any object because it operates in the region of the spectrum where the thermal contrast is much higher due to black body physics. This allows TIR to be used in general visual object tracking for grayscale sequences [ 2 ]. As a consequence, any effective general image tracker, such as those using RGB images, is capable of TIR tracking, and this has led to the development of many TIR applications, including pedestrian detection and object tracking [ 3 ].…”