Image Restoration with Point Spread Function Regularization and Active Learning

TL;DR

This paper introduces a novel deep learning-based image restoration method that utilizes a telescope simulator for training, significantly improving the quality of astronomical images affected by noise and blur, thereby aiding large-scale sky surveys.

Contribution

The paper presents a new image restoration algorithm that combines deep learning with high-fidelity telescope simulation for effective astronomical image enhancement.

Findings

Effective enhancement of fine structures in blurry images

Improved quality of observation images from real and simulated data

Applicable to large-scale sky survey datasets

Abstract

Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal 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 directly restore images obtained by the telescope, 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 LSST, Euclid, and CSST, to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.