跨巡天图像转换：将SDSS和DECaLS图像增强至接近HSC质量以用于高级天文分析
Cross-Survey Image Transformation: Enhancing SDSS and DECaLS Images to Near-HSC Quality for Advanced Astronomical Analysis
AI总结：
提出Pix2WGAN混合模型，结合pix2pix与WGAN-GP，将SDSS和DECaLS图像增强至接近HSC质量，显著提升复杂结构识别，并在多数指标上优于原始图像。
AI中文摘要：
本研究聚焦于天文巡天之间的星系图像转换，特别是将斯隆数字巡天（SDSS）和暗能量相机遗产巡天（DECaLS）的图像增强至与超新星相机巡天（HSC）相当的质量。我们提出了一种名为Pix2WGAN的混合模型，该模型将pix2pix框架与带梯度惩罚的Wasserstein生成对抗网络（WGAN-GP）相结合，将低质量的观测图像转换为高质量图像。我们的模型成功地将DECaLS图像转换为伪HSC图像，取得了令人印象深刻的结果，并显著增强了复杂结构（如星系旋臂和潮汐尾）的识别能力，这些结构在原始DECaLS图像中可能被忽略。此外，Pix2WGAN有效解决了源图像和目标图像中的伪影、噪声和模糊等问题。在基础Pix2WGAN模型之外，我们进一步开发了一种名为级联Pix2WGAN的先进架构，该架构引入了多阶段训练机制，旨在弥合SDSS与HSC图像之间的质量差距，并展示了同样有前景的结果。我们使用多种指标系统评估了模型生成的伪HSC图像与实际HSC图像之间的相似性，包括均方根误差（RMSE）、峰值信噪比（PSNR）和结构相似性指数（SSIM），以及感知指标如学习感知图像块相似性（LPIPS）和Fréchet初始距离（FID）。结果表明，经我们模型转换的图像在几乎所有评估指标上均优于原始SDSS和DECaLS图像。我们的研究预计将为天文数据分析、跨巡天图像集成和高精度天体测量提供重要的技术支持。
英文摘要：
This study focuses on transforming galaxy images between astronomical surveys, specifically enhancing images from the Sloan Digital Sky Survey (SDSS) and the Dark Energy Camera Legacy Survey (DECaLS) to achieve quality comparable to the Hyper Suprime-Cam survey (HSC). We proposed a hybrid model called Pix2WGAN, which integrates the pix2pix framework with the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) to convert low-quality observational images into high-quality counterparts. Our model successfully transformed DECaLS images into pseudo-HSC images, yielding impressive results and significantly enhancing the identification of complex structures, such as galaxy spiral arms and tidal tails, which may have been overlooked in the original DECaLS images. Moreover, Pix2WGAN effectively addresses issues like artifacts, noise, and blurriness in both source and target images. In addition to the basic Pix2WGAN model, we further developed an advanced architecture called Cascaded Pix2WGAN, which incorporates a multi-stage training mechanism designed to bridge the quality gap between SDSS and HSC images, demonstrating similarly promising outcomes. We systematically assessed the similarity between the model-generated pseudo-HSC images and actual HSC images using various metrics, including Root Mean Squared Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), along with perceptual metrics such as Learned Perceptual Image Patch Similarity (LPIPS) and Fréchet Inception Distance (FID). The results indicate that images transformed by our model outperform both the original SDSS and DECaLS images across nearly all evaluation metrics. Our research is expected to provide significant technical support for astronomical data analysis, cross-survey image integration, and high-precision astrometry.
