A new iterative algorithm is proposed to improve the detection of dim stellar objects that are in the neighbourhood of a bright object, using short-exposure images. This method separates data functions into the primary bright object function, the neighbourhood system function, and the background function. This approach uses the principles of the Expectation-Maximization algorithm with the Gerchberg-Saxton phase retrieval algorithm to overcome the image degradation caused by the photon counting noise from the charge-coupled devices and the turbulent atmospheric conditions. The performance of this new neighbourhood system algorithm is compared with that of the multiframe blind deconvolution algorithm, using laboratory data and computer-simulated data. This paper provides an improved technique to image closely spaced dim objects.

中文翻译：

---

通过近恒星邻域的多帧盲反卷积改进紧密间隔的暗淡物体检测

提出了一种新的迭代算法，以使用短曝光图像改进对明亮物体附近的暗淡恒星物体的检测。该方法将数据函数分为主要亮目标函数、邻域系统函数和背景函数。该方法使用期望最大化算法的原理和 Gerchberg-Saxton 相位检索算法来克服由电荷耦合器件的光子计数噪声和湍流大气条件引起的图像质量下降。使用实验室数据和计算机模拟数据，将这种新的邻域系统算法的性能与多帧盲反卷积算法的性能进行了比较。本文提供了一种改进的技术来对紧密间隔的昏暗物体进行成像。