Visible and Infrared Image Fusion-Based Image Quality Enhancement with Applications to Space Debris On-Orbit Surveillance

Abstract

The increasing amount of space debris in recent years has greatly threatened space operation. In order to ensure the safety level of spacecraft, space debris perception via on-orbit visual sensors has become a promising solution. However, the perception capability of visual sensors largely depends on illumination, which tends to be insufficient in dark environments. Since the images captured by visible and infrared sensors are highly complementary in dark environments, a convolutional sparse representation-based visible and infrared image fusion algorithm is proposed in this paper to expand the applicability of visual sensors. In particular, the local contrast measure is applied to obtain the refined weight map for fusing the base layers, which is more robust in a dark space environment. The algorithm can settle two significant problems in space debris surveillance, namely, improving the signal-noise ratio in a noise space environment and preserving more detailed information in a dark space environment. A space debris dataset containing registered visible and infrared images has been purposely created and used for algorithm evaluation. Experimental results demonstrate that the proposed method in this paper is effective for enhancing image qualities and can achieve favorable effects compared to other state-of-the-art algorithms.

BDPartNet: Feature Decoupling and Reconstruction Fusion Network for Infrared and Visible Image

“…Image fusion is an important image processing task, aiming to merge images from different sensors or bands into a single composite image, which has important applications and demands in multiple fields [1][2][3]. For instance, it plays an important role in spacecraft relative navigation, among other diverse application areas [4]. In addition, in the field of remote sensing, image fusion is instrumental for tasks such as detection with small infrared targets [5], ship detection in remote sensing image [6][7][8].…”

Spacecraft and Asteroid Thermal Image Generation for Proximity Navigation and Detection Scenarios

“…These are just the two biggest advantages of thermal images; many other applications are made possible thanks to thermal infrared (TIR) images such as site selection in dark regions, deployable satellite tracking and navigation, and target marking [1]. Indeed, there is novel literature on the application of thermal infrared images for navigation in adverse illumination conditions in proximity scenarios, for example, for active debris removal [4][5][6]. In the same context, an interesting application of TIR images is their combination with visible (VIS) images using fusion algorithms to retain the advantages of both sensors (i.e., high definition for VIS and robustness in low illumination conditions for TIR); on this matter the interested reader can refer to [2,7,8].…”