Image restoration with point-spread function regularization and active learning

Abstract

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.

CSST Strong-lensing Preparation: A Framework for Detecting Strong Lenses in the Multicolor Imaging Survey by the China Survey Space Telescope (CSST)

“…Subsequent to training, the RESTORE neural network could be used directly to restore blurred images. In accordance with the functionalities of these two networks, we adjust the loss function of these neural networks as depicted in Equation (9), following the method proposed in Lv et al (2022) and Jia et al (2024). Here L idet signifies the identity loss function, L rec denotes the cyclic loss function, and L fl stands for the focal frequency loss function:…”

“…As discussed earlier, in order to obtain scientific outcomes with acceptable costs from observation data collected by CSST, there is an urgent need to enhance the detection capability of our algorithm. Given the impact of varying levels of noise and variable PSFs on images, one strategy involves leveraging image restoration algorithms such as the PSF-Net proposed by Lv et al (2022) and Jia et al (2024). This involves training the PSF-Net with CSST PSFs and subsequently employing it to enhance the quality of observation images.…”