Real-time multiframe blind deconvolution of solar images

TL;DR

This paper introduces deep learning architectures that enable real-time blind deconvolution of solar images, significantly speeding up image correction and preserving photometric and polarimetric properties for ground-based solar observations.

Contribution

The paper presents two novel deep learning models that perform fast, high-quality blind deconvolution of solar images in real time, improving upon traditional methods.

Findings

Achieved ~100 images/sec correction rate.

Produced high-quality, noise-suppressed images.

Maintained photometric and polarimetric integrity.

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.