如何叠加图像：II. 抗混叠与PSF反卷积
How to coadd images: II. Anti-aliasing and PSF deconvolution
AI总结：
本文提出一种结合迭代重加权最小二乘与分治算法的新图像叠加方法，可同时实现抗混叠和PSF反卷积，在恢复延展源、提高PSNR和减少振铃伪影方面优于现有算法，且不依赖PSF局部均匀假设，但计算开销更大。
摘要：
我们开发了一种新颖的方法，用于叠加多个欠采样图像，该方法结合了迭代重加权最小二乘法和分治算法。我们的方法不仅能够对图像进行抗混叠处理，还能实现PSF反卷积，从而增强对延展源的恢复，获得最高的PSNR，并减少振铃伪影。为了测试我们的方法，我们进行了数值模拟，复现了CSST/VST望远镜的观测运行，并将我们的结果与使用先前算法得到的结果进行了比较。模拟表明，我们的方法在多个方面优于先前的方法，例如恢复延展源的轮廓和最小化振铃伪影。此外，由于我们的方法依赖于最小二乘拟合的固有优势，它更加通用，并且不依赖于PSF的局部均匀性假设。然而，新方法比其他方法消耗更多的计算资源。
英文摘要：
We have developed a novel method for co-adding multiple under-sampled images that combines the iteratively reweighted least squares and divide-and-conquer algorithms. Our approach not only allows for the anti-aliasing of the images but also enables PSF deconvolution, resulting in enhanced restoration of extended sources, the highest PSNR, and reduced ringing artefacts. To test our method, we conducted numerical simulations that replicated observation runs of the CSST/VST telescope and compared our results to those obtained using previous algorithms. The simulation showed that our method outperforms previous approaches in several ways, such as restoring the profile of extended sources and minimizing ringing artefacts. Additionally, because our method relies on the inherent advantages of least squares fitting, it is more versatile and does not depend on the local uniformity hypothesis for the PSF. However, the new method consumes much more computation than the other approaches.
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