# Nonuniform Background Correction for Wide-Field Surveillance Camera
## 1 Introduction
There are many filtering algorithms, such as max-mean filter [22], mean iterative filtering [23], two-dimensional least mean square (tdlms) filter [24; 25; 26], and new top-hat transform [27; 28]. These methods cannot both have high-accuracy correction of stray light nonuniform background and high-precision of target retention rate in the surveillance image [29]. Therefore, [PERSON] et al. proposed an improved new top-hat transformation (INTHT) [29], which uses two different but related structural elements based on the new top-hat transform to retrieve the lost targets for a wide-field surveillance system in recent years. Moreover, it has higher accuracy and effectiveness in correcting a stray light nonuniform background. [PERSON] et al. proposed an accurate stray light elimination method based on recursion multi-scale gray-scale morphology [30] for wide-field surveillance cameras in recent years. The algorithm adopts a recursion multi-scale method; that is, it increases the size of the structural operators to ensure that large stars and space targets will not be lost. This method can simultaneously have a high-precision stray light elimination effect and a high-precision target retention rate. They all have achieved good results in the stray light nonuniform background correction for wide-field surveillance. However, they rely far too much on structural elements for background suppression, resulting in the loss of space targets or stars, so their proposed methods will lose part of the weak targets. As a result, their detection rate is less than 100%. That is because, in the actual space surveillance image, in order to obtain a smooth background, it is often necessary to use larger structural elements to suppress the stray light noise. Meanwhile, the weak targets disappear. They all do not fundamentally overcome the dependence and limitations of the background suppression effect of the new top-hat transformation on the selection of structural elements. The nonuniform background of a wide-field imaging system is complex, which is usually caused by coupler devices such as optical fiber panels, dark currents, and stray light. This paper considers that these factors are important components of the nonuniform background, which seriously degrades the image quality. The first step of space target detection is star map matching. Therefore, we need to achieve nonuniform background correction and accurate star point segmentation, which will greatly help star point centroid positioning accuracy. Itbrings great help to space-moving target recognition and tracking. At present, the existing algorithms do not consider comprehensive facts when processing surveillance images. For weak targets, the processing ability of these algorithms is poor, and the processed images often have a large amount of residual background noise. That is to say, they cannot achieve accurate segmentation of weak targets while achieving a higher precision of nonuniform background correction. To solve this problem, we improve the new top-hat transform by designing a noise structure element (NSE). The introduction of NSE separates the function of stray light nonuniform background suppression and noise suppression, which breaks traditional structure element selection limitations and achieves an ideal processing effect between retaining targets and suppressing nonuniform background. On the one hand, the ENTHT algorithm achieves better retention of weak targets. On the other hand, it can better suppress nonuniform noise and achieve more accurate weak target segmentation. The experimental results show that our algorithm still performs well under the worst condition. It fully demonstrates the feasibility and superiority of this algorithm in surveillance nonuniform background correction.