背景推导和图像平坦化：getimages
Background derivation and image flattening: getimages
摘要：
空间观测站获得的现代高分辨率图像显示，所有空间尺度上的图像都存在极强的强度变化。在这样的图像中，使用基于全局阈值的方法进行源提取可能会在明亮区域中带来不可接受的大量虚假源，而无法检测低背景或低噪声区域中的源。在提取源或细丝之前减去背景并均衡图像中的小尺度波动的水平将是非常有益的。本文介绍了一种新的背景提取和图像平坦化方法getimages。它是基于中值滤波与滑动窗口，对应于一系列的空间尺度从观测光束的大小到最大结构宽度$X_λ$。后者是getimages的单个自由参数，可以从观察到的图像$\mathcal{I}_λ$手动评估。中值滤波算法为低于$X_λ$的所有宽度的结构提供背景图像$\tilde{\mathcal{B}}_λ$。应用于从减去背景的图像$\tilde{\mathcal{S}}_λ$导出的标准偏差$\mathcal{D}_λ$的图像的相同中值滤波过程导致平坦化图像$\tilde{\mathcal{F}}_λ$。最后，计算平坦化的检测图像$\mathcal{I}_{λ\mathrm{D}}{\,=\,}\tilde{\mathcal{S}}_λ{/}\tilde{\mathcal{F}}_λ$，其标准偏差是均匀的外部源和细丝。在这种大大简化的图像中检测源导致更完整和可靠的更干净的提取。作为奖励，getimages减少了各种观测和地图制作的伪影，并均衡了镶嵌图像的独立瓷砖之间的噪声水平。
英文摘要：
Modern high-resolution images obtained with space observatories display extremely strong intensity variations across images on all spatial scales. Source extraction in such images with methods based on global thresholding may bring unacceptably large numbers of spurious sources in bright areas while failing to detect sources in low-background or low-noise areas. It would be highly beneficial to subtract background and equalize the levels of small-scale fluctuations in the images before extracting sources or filaments. This paper describes getimages, a new method of background derivation and image flattening. It is based on median filtering with sliding windows that correspond to a range of spatial scales from the observational beam size up to a maximum structure width $X_λ$. The latter is a single free parameter of getimages that can be evaluated manually from the observed image $\mathcal{I}_λ$. The median filtering algorithm provides a background image $\tilde{\mathcal{B}}_λ$ for structures of all widths below $X_λ$. The same median filtering procedure applied to an image of standard deviations $\mathcal{D}_λ$ derived from a background-subtracted image $\tilde{\mathcal{S}}_λ$ results in a flattening image $\tilde{\mathcal{F}}_λ$. Finally, a flattened detection image $\mathcal{I}_{λ\mathrm{D}}{\,=\,}\tilde{\mathcal{S}}_λ{/}\tilde{\mathcal{F}}_λ$ is computed, whose standard deviations are uniform outside sources and filaments. Detecting sources in such greatly simplified images results in much cleaner extractions that are more complete and reliable. As a bonus, getimages reduces various observational and map-making artifacts and equalizes noise levels between independent tiles of mosaicked images.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
