论文标题
深度学习的星系图像反卷积
Deep Learning-based galaxy image deconvolution
论文作者
论文摘要
随着大规模天文调查的发作，捕获了数百万张图像，越来越需要开发快速准确的反卷积算法，可以很好地推广到不同图像。一种功能强大且易于使用的反卷积方法将允许重建对天空的更清洁估计。反浏览图像将有助于执行光度测量，以帮助在星系形成和进化领域取得进展。我们提出了一种基于Learnet Transform的新卷积方法。最终，我们通过遵循两步方法的方法来研究和比较天体物理领域中不同的UNET架构和学习图像对卷积的性能：带有封闭式解决方案的Tikhonov Deonvolution，然后使用神经网络进行后处理。为了生成我们的培训数据集，我们从F606W过滤器（V波段）中提取HST切口，并损坏这些图像以模拟其模糊的噪声版本。我们基于这些模拟的数值结果显示了不同噪声水平的考虑方法之间的详细比较。
With the onset of large-scale astronomical surveys capturing millions of images, there is an increasing need to develop fast and accurate deconvolution algorithms that generalize well to different images. A powerful and accessible deconvolution method would allow for the reconstruction of a cleaner estimation of the sky. The deconvolved images would be helpful to perform photometric measurements to help make progress in the fields of galaxy formation and evolution. We propose a new deconvolution method based on the Learnlet transform. Eventually, we investigate and compare the performance of different Unet architectures and Learnlet for image deconvolution in the astrophysical domain by following a two-step approach: a Tikhonov deconvolution with a closed-form solution, followed by post-processing with a neural network. To generate our training dataset, we extract HST cutouts from the CANDELS survey in the F606W filter (V-band) and corrupt these images to simulate their blurred-noisy versions. Our numerical results based on these simulations show a detailed comparison between the considered methods for different noise levels.
