The Transformer architecture has revolutionized the field of deep learning over the past several years in diverse areas, including natural language processing, code generation, image recognition, and time-series forecasting. We propose to apply Zamir et al.'s efficient transformer to perform deconvolution and denoising to enhance astronomical images. We conducted experiments using pairs of high-quality images and their degraded versions, and our deep learning model demonstrates exceptional restoration of photometric, structural, and morphological information. When compared with the ground-truth James Webb Space Telescope images, the enhanced versions of our Hubble Space Telescope–quality images reduce the scatter of isophotal photometry, Sérsic index, and half-light radius by factors of 4.4, 3.6, and 4.7, respectively, with Pearson correlation coefficients approaching unity. The performance is observed to degrade when input images exhibit correlated noise, point-like sources, and artifacts. We anticipate that this deep learning model will prove valuable for a number of scientific applications, including precision photometry, morphological analysis, and shear calibration.

中文翻译：

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更深、更锐、更快：高效变压器在星系图像修复中的应用


过去几年，Transformer 架构在自然语言处理、代码生成、图像识别和时间序列预测等多个领域彻底改变了深度学习领域。我们建议应用 Zamir 等人的高效转换器来执行反卷积和去噪以增强天文图像。我们使用成对的高质量图像及其降级版本进行了实验，我们的深度学习模型展示了光度、结构和形态信息的出色恢复。与真实的詹姆斯·韦伯太空望远镜图像相比，我们的哈勃太空望远镜质量图像的增强版本将等光测光、Sérsic 指数和半光半径的散射分别减少了 4.4、3.6 和 4.7 倍，皮尔逊相关系数接近 1。当输入图像表现出相关噪声、点状源和伪影时，观察到性能会下降。我们预计这种深度学习模型将对许多科学应用具有价值，包括精密光度测定、形态分析和剪切校准。