BSC-Net: Background Suppression Algorithm for Stray Lights in Star Images

Abstract

Most background suppression algorithms are weakly robust due to the complexity and fluctuation of the star image’s background. In this paper, a background suppression algorithm for stray lights in star images is proposed, which is named BSC-Net (Background Suppression Convolutional Network) and consist of two parts: “Background Suppression Part” and “Foreground Retention Part”. The former part achieves background suppression by extracting features from various receptive fields, while the latter part achieves foreground retention by merging multi-scale features. Through this two-part design, BSC-Net can compensate for blurring and distortion of the foreground caused by background suppression, which is not achievable in other methods. At the same time, a blended loss function of smooth_L1&Structure Similarity Index Measure (SSIM) is introduced to hasten the network convergence and avoid image distortion. Based on the BSC-Net and the loss function, a dataset consisting of real images will be used for training and testing. Finally, experiments show that BSC-Net achieves the best results and the largest Signal-to-Noise Ratio (SNR) improvement in different backgrounds, which is fast, practical and efficient, and can tackle the shortcomings of existing methods.

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

“…SExtractor [50] is a commonly used astronomical software. DnCNN [51] and BSC-Net [29] are background suppression algorithms based on deep learning. Our algorithm mainly improves the signal-to-noise ratio of the target and achieves image enhancement by suppressing the complex background.…”

“…proposed a two-stage algorithm, which used the reinforcement learning idea to realize the iterative denoising operation in the star point region [28]. Li et al (2022) proposed a background suppression algorithm, BSC-Net (Background Suppression Convolutional Network), for astral stray light, which consists of a "background suppression part" and a "foreground preservation part", and yields an acceptable background suppression effect on the star map [29].…”

“…We know that the effect of the neural network is very dependent on the quality of the data as well as the labels; therefore, in the process of data labeling, we adopt the current industry's more advanced methods and choose traditional image processing methods such as Top-Hat [43] and morphological filter [44] to obtain relatively clean labels. The specific processing flow can be found in the literature [29] and Figure 5 below. The experimental data are generally divided into two categories, with a total of four data sets.…”

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.…”