  The study of astronomical phenomena through ground-based observations is
always challenged by the distorting effects of Earth's atmosphere. Traditional
methods of post-facto image correction, essential for correcting these
distortions, often rely on simplifying assumptions that limit their
effectiveness, particularly in the presence of spatially variant atmospheric
turbulence. Such cases are often solved by partitioning the field-of-view into
small patches, deconvolving each patch independently, and merging all patches
together. This approach is often inefficient and can produce artifacts. Recent
advancements in computational techniques and the advent of deep learning offer
new pathways to address these limitations. This paper introduces a novel
framework leveraging a deep neural network to emulate spatially variant
convolutions, offering a breakthrough in the efficiency and accuracy of
astronomical image deconvolution. By training on a dataset of images convolved
with spatially invariant point spread functions and validating its
generalizability to spatially variant conditions, this approach presents a
significant advancement over traditional methods. The convolution emulator is
used as a forward model in a multi-object multi-frame blind deconvolution
algorithm for solar images. The emulator enables the deconvolution of solar
observations across large fields of view without resorting to patch-wise
mosaicking, thus avoiding artifacts associated with such techniques. This
method represents a significant computational advantage, reducing processing
times by orders of magnitude.
