# Nonuniform Background Correction for Wide-Field Surveillance Camera
## 1 Introduction  
With the development of science and technology, space exploration is becoming more and more frequent. The number of space targets has constantly been increasing since Sputnik-1 was launched in 1957 [1; 2; 3]. Only a small portion of the detected space targets are active satellites, and the rest can be regarded as space debris. Such a large number of space targets will pose a remarkable threat to the safety of spacecraft and human space activities [4; 5]. In February of 2009, an active Iridium satellite collided with a deceased Cosmos satellite in LEO [6]. The resulting debris cloud contains hundreds of objects large enough to be tracked and potentially thousands of smaller objects that are still large enough to disable another spacecraft [7]. In order to predict and avoid these threats, it is very important to detect space targets. Space environment surveillance is very important for space security [8]. A wide-field surveillance camera can detect unidentified objects in space well. However, it is easy to be disturbed by stray light and hot pixels, and the image background presents a certain degree of nonuniformity, which makes the target signal unable to be segmented effectively. Stray light is one of the major aspects impacting the performance of optical sensors [9; 10; 11], which seriously affects the imaging quality and detection ability of the wide-field surveillance camera [12; 13; 14]. Therefore, determining how to correct the stray light nonuniform background effectively is very important for wide-field surveillance. Stray light is usually suppressed through theoretical analysis and simulation in the process of space load design. A well-designed lens baffle that considers the working orbitand exact space task can help the wide-field surveillance camera overcome most stray light problems [15]. For example, [PERSON] [16] analyzed the orbit characteristics and solar incidence angle distribution of a sun-synchronous orbit satellite and designed a baffle to avoid direct sunlight. However, it is often difficult to eliminate the influence of stray light in a complex space environment [17]. The strong stray light will still be received by the detector after an optical buffle. For better imaging performance, we need wide-field surveillance image post-processing. At present, scholars have studied many nonuniformity correction algorithms in imaging systems. Flat field calibration techniques are the correction of spatial inhomogeneities in the optical sensitivity of pixel elements, which can correct the nonuniformity of camera response (PRNU). This nonuniformity is caused by the difference in photosensitive source response, noise level, quantum efficiency, etc. At the same time, if the imaging system uses coupling devices such as a fiber panel or fiber cone lens [18], it will also bring serious nonuniformity to the imaging system. These are all static scene correction algorithms; that is to say, when a camera is manufactured, these nonuniformities are fixed, and these algorithms cannot eliminate the interference of uncertain stray light [19]. Scene-based nonuniformity correction algorithms are widely used since they only need the readout image data. [PERSON] et al. [20] proposed a method based on the neural network algorithm for real-time correction using the framework of foreground and background. [PERSON] et al. [21] proposed a novel binarization method for nonuniformly illumination based on the curvelet transform. These neural network methods and frequency-domain methods are too complicated and need huge calculations. The amount of data taken by our camera for each task is up to hundreds of gigabytes. When the algorithm is complex, processing these images requires a lot of computing time. Space threat assessment has a high requirement for the real-time performance of the system, so the algorithms related to neural networks are not applicable to our system.