河外成像巡天去噪算法的比较分析
A comparative analysis of denoising algorithms for extragalactic imaging surveys
摘要：
我们提出了一个全面的分析降噪（“降噪”）算法的性能，以确定它们是否提供优势，在源检测河外调查图像。分析的方法有Perona-Malik滤波，双边滤波，总变分去噪，结构-纹理图像分解，非局部均值，小波变换和块匹配。我们测试了算法的模拟图像的河外领域的分辨率和深度典型的哈勃，斯皮策和欧几里得空间望远镜，和地面仪器。在选择了最佳的内部参数配置后，我们评估了它们的性能作为分辨率，背景水平和图像类型的函数，也测试了它们保持物体通量和形状的能力。我们分析的完整性和纯度的目录应用去噪算法后提取的模拟欧几里德宽调查VIS图像，真正的H160（HST）和K-波段（HAWK-I）的观测CANDELS GOODS南字段。去噪算法通常优于用图像的点扩散函数（PSF）进行滤波的标准方法。应用结构纹理图像分解，Perona-Malik滤波，Chambolle的总变分方法，和双边滤波的Euclid-VIS图像，我们得到的目录都是更纯粹和完整的0.2个数量级比那些基于标准的方法。将结构-纹理图像分解算法应用于H160图像，取得了相同的结果。去噪技术相对于PSF滤波的优势随着深度的增加而增加。此外，这些技术相对于PSF平滑更好地保留了检测到的对象的形状。去噪算法在微弱天体的探测方面提供了显着的改进，并提高了当前和未来河外调查的科学回报。
英文摘要：
We present a comprehensive analysis of the performance of noise-reduction (``denoising'') algorithms to determine whether they provide advantages in source detection on extragalactic survey images. The methods under analysis are Perona-Malik filtering, Bilateral filter, Total Variation denoising, Structure-texture image decomposition, Non-local means, Wavelets, and Block-matching. We tested the algorithms on simulated images of extragalactic fields with resolution and depth typical of the Hubble, Spitzer, and Euclid Space Telescopes, and of ground-based instruments. After choosing their best internal parameters configuration, we assess their performance as a function of resolution, background level, and image type, also testing their ability to preserve the objects fluxes and shapes. We analyze in terms of completeness and purity the catalogs extracted after applying denoising algorithms on a simulated Euclid Wide Survey VIS image, on real H160 (HST) and K-band (HAWK-I) observations of the CANDELS GOODS-South field. Denoising algorithms often outperform the standard approach of filtering with the Point Spread Function (PSF) of the image. Applying Structure-Texture image decomposition, Perona-Malik filtering, the Total Variation method by Chambolle, and Bilateral filtering on the Euclid-VIS image, we obtain catalogs that are both more pure and complete by 0.2 magnitudes than those based on the standard approach. The same result is achieved with the Structure-Texture image decomposition algorithm applied on the H160 image. The advantage of denoising techniques with respect to PSF filtering increases at increasing depth. Moreover, these techniques better preserve the shape of the detected objects with respect to PSF smoothing. Denoising algorithms provide significant improvements in the detection of faint objects and enhance the scientific return of current and future extragalactic surveys.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
