We present a new algorithm for estimating the Point Spread Function (PSF) in wide-field astronomical images with extreme source crowding. Robust and accurate PSF estimation in crowded astronomical images dramatically improves the fidelity of astrometric and photometric measurements extracted from wide-field sky monitoring imagery. Our radically new approach utilizes convolutional sparse representations to model the continuous functions involved in the image formation. This approach avoids the need to detect and precisely localize individual point sources that is shared by existing methods. In experiments involving simulated astronomical imagery, it significantly outperforms the recent alternative method with which it is compared. This is an extended version of a letter accepted for publication, with supplemental material included as appendices.

中文翻译：

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拥挤天文图像中的 PSF 估计作为卷积字典学习问题

我们提出了一种新算法，用于估计具有极端源拥挤的宽视场天文图像中的点扩展函数 (PSF)。在拥挤的天文图像中稳健而准确的 PSF 估计显着提高了从宽视场天空监测图像中提取的天体测量和光度测量的保真度。我们全新的方法利用卷积稀疏表示对图像形成中涉及的连续函数进行建模。这种方法避免了检​​测和精确定位现有方法共享的单个点源的需要。在涉及模拟天文图像的实验中，它显着优于最近与之进行比较的替代方法。这是一封已接受出版的信函的扩展版本，补充材料包含在附录中。