论文标题
带有tikhonov正则化域上的deblurring星系图像
Deblurring galaxy images with Tikhonov regularization on magnitude domain
论文作者
论文摘要
我们提出了一种基于正则化的脱毛方法，该方法有效地适用于星系图像。地面望远镜的空间分辨率通常受到看法的限制，并且比空间望远镜更糟。这种情况对恢复空间分辨率产生了相当大的研究兴趣。由于图像脱毛是一个典型的逆问题，而且通常不适合，因此解决方案往往是不稳定的。为了获得稳定的解决方案，许多研究采用了基于正规化的图像去蓝色方法，但是正则化项不一定适合银河系图像。尽管星系具有指数型或刻度剖面，但常规正则化假设图像曲线在空间中的线性行为。假设和真实情况之间的显着偏差导致模糊图像并平滑详细的结构。显然，对数的正则化，即幅度域，应提供一个更合适的假设，我们在本研究中探讨了这一点。我们通过在幅度域上具有Tikhonov正则化项的目标函数来提出脱毛星系图像的问题。我们引入了一种迭代算法，通过一种原始的二分解方法最大程度地降低了目标函数。我们使用仿真和观察图像研究了所提出的方法的可行性。在模拟中，我们将带有逼真的点扩散功能的星系图像模糊，并同时添加高斯和泊松噪声。对于观察到的图像的评估，我们使用Subaru HSC-SSP拍摄的星系图像。这两种评估都表明，我们的方法成功地恢复了图像的空间分辨率，并显着优于常规方法。该代码可从GitHub（https://github.com/kzmurata-astro/psfdeconv_amag）公开获得。
We propose a regularization-based deblurring method that works efficiently for galaxy images. The spatial resolution of a ground-based telescope is generally limited by seeing conditions and much worse than space-based telescopes. This circumstance has generated considerable research interest in restoration of spatial resolution. Since image deblurring is a typical inverse problem and often ill-posed, solutions tend to be unstable. To obtain a stable solution, much research has adopted regularization-based methods for image deblurring, but the regularization term is not necessarily appropriate for galaxy images. Although galaxies have an exponential or Sersic profile, the conventional regularization assumes the image profiles to behave linear in space. The significant deviation between the assumption and real situation leads to blurring the images and smoothing out the detailed structures. Clearly, regularization on logarithmic, i.e. magnitude domain, should provide a more appropriate assumption, which we explore in this study. We formulate a problem of deblurring galaxy images by an objective function with a Tikhonov regularization term on magnitude domain. We introduce an iterative algorithm minimizing the objective function with a primal-dual splitting method. We investigate the feasibility of the proposed method using simulation and observation images. In the simulation, we blur galaxy images with a realistic point spread function and add both Gaussian and Poisson noises. For the evaluation with the observed images, we use galaxy images taken by the Subaru HSC-SSP. Both of these evaluations show that our method successfully recovers the spatial resolution of the images and significantly outperforms the conventional methods. The code is publicly available from the Github ( https://github.com/kzmurata-astro/PSFdeconv_amag ).
