Multi-Level Convolutional Network for Ground-Based Star Image Enhancement

Abstract

The monitoring of space debris is important for spacecraft such as satellites operating in orbit, but the background in star images taken by ground-based telescopes is relatively complex, including stray light caused by diffuse reflections from celestial bodies such as the Earth or Moon, interference from clouds in the atmosphere, etc. This has a serious impact on the monitoring of dim and small space debris targets. In order to solve the interference problem posed by a complex background, and improve the signal-to-noise ratio between the target and the background, in this paper, we propose a novel star image enhancement algorithm, MBS-Net, based on background suppression. Specifically, the network contains three parts, namely the background information estimation stage, multi-level U-Net cascade module, and recursive feature fusion stage. In addition, we propose a new multi-scale convolutional block, which can laterally fuse multi-scale perceptual field information, which has fewer parameters and fitting capability compared to ordinary convolution. For training, we combine simulation and real data, and use parameters obtained on the simulation data as pre-training parameters by way of parameter migration. Experiments show that the algorithm proposed in this paper achieves competitive performance in all evaluation metrics on multiple real ground-based datasets.

A Practical Star Image Registration Algorithm Using Radial Module and Rotation Angle Features

“…Current methods for space environment situational awareness [1][2][3][4][5], near-Earth asteroid detection [6], large-scale sky survey [7,8], etc., yield massive amounts of star image data every day. Star image registration is a fundamental data processing step for astronomical image differencing [9], stacking [10], and mosaicking [11] applications, and has high requirements in terms of robustness, accuracy, and real-time capability on the part of registration algorithms.…”

A Practical Star Image Registration Algorithm Using Radial Module and Rotation Angle Features

“…The current space environment situational awareness [1][2][3][4][5],near-Earth asteroid detection [6], and large-scale sky survey [7,8] etc. yield massive amount of star image data every day.…”