  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.
