Real-time multiframe blind deconvolution of solar images

Abstract

The quality of images of the Sun obtained from the ground are severely limited by the perturbing effect of the turbulent Earth's atmosphere. The post-facto correction of the images to compensate for the presence of the atmosphere require the combination of high-order adaptive optics techniques, fast measurements to freeze the turbulent atmosphere and very time consuming blind deconvolution algorithms. Under mild seeing conditions, blind deconvolution algorithms can produce images of astonishing quality. They can be very competitive with those obtained from space, with the huge advantage of the flexibility of the instrumentation thanks to the direct access to the telescope. In this contribution we leverage deep learning techniques to significantly accelerate the blind deconvolution process and produce corrected images at a peak rate of ~100 images per second. We present two different architectures that produce excellent image corrections with noise suppression while maintaining the photometric properties of the images. As a consequence, polarimetric signals can be obtained with standard polarimetric modulation without any significant artifact. With the expected improvements in computer hardware and algorithms, we anticipate that on-site real-time correction of solar images will be possible in the near future.

arXiv comment16 pages, 12 figures, accepted for publication in A&A

References in corpus (11)

• Sunrise: instrument, mission, data and first results
• The Imaging Magnetograph eXperiment (IMaX) for the Sunrise balloon-borne solar observatory
• A catalog of visual-like morphologies in the 5 CANDELS fields using deep-learning
• Generative Adversarial Networks recover features in astrophysical images of galaxies beyond the deconvolution limit
• CRISPRED: A data pipeline for the CRISP imaging spectropolarimeter
• Multi-frame blind deconvolution with linear equality constraints
• DeepVel: deep learning for the estimation of horizontal velocities at the solar surface
• On fibrils and field lines: The nature of H$\alpha$ fibrils in the solar chromosphere
• Enhancing SDO/HMI images using deep learning
• Analysis of Seeing-Induced Polarization Cross-Talk and Modulation Scheme Performance
• H-alpha features with hot onsets. II. A contrail fibril

Cited by in corpus (15)

• Stokes Inversion based on Convolutional Neural Networks
• Instrumentation for solar spectropolarimetry: state of the art and prospects
• Fast Solar Image Classification Using Deep Learning and its Importance for Automation in Solar Physics
• Coordinate-based neural representations for computational adaptive optics in widefield microscopy
• Solar image denoising with convolutional neural networks
• Mimicking spectropolarimetric inversions using convolutional neural networks
• Solar Image Restoration with the Cycle-GAN Based on Multi-Fractal Properties of Texture Features
• Learning to do multiframe wavefront sensing unsupervisedly: applications to blind deconvolution
• Improved detection of farside solar active regions using deep learning
• Instrument-To-Instrument translation: Instrumental advances drive restoration of solar observation series via deep learning
• Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D): I. Overview, Magnetohydrodynamic Modeling, and Stokes Profile Synthesis
• Deep learning image burst stacking to reconstruct high-resolution ground-based solar observations
• Accelerating Multiframe Blind Deconvolution via Deep Learning
• A Machine Learning Approach to Correcting Atmospheric Seeing in Solar Flare Observations
• Multi-frame blind deconvolution and phase diversity with statistical inclusion of uncorrected high-order modes