@Article{CSIAM-AM-1-3,
author = {Wang, Na and Sun, Jian},
title = {Model Meets Deep Learning in Image Inverse Problems},
journal = {CSIAM Transactions on Applied Mathematics},
year = {2020},
volume = {1},
number = {3},
pages = {365--386},
abstract = {<p style="text-align: justify;">Image inverse problem aims to reconstruct or restore high-quality images
from observed samples or degraded images, with wide applications in imaging sciences. The traditional methods rely on mathematical models to invert the process of
image sensing or degradation. But these methods require good design of image prior
or regularizer that is hard to be hand-crafted. In recent years, deep learning has been
introduced to image inverse problems by learning to invert image sensing or degradation process. In this paper, we will review a new trend of methods for image inverse
problem that combines the imaging/degradation model with deep learning approach.
These methods are typically designed by unrolling some optimization algorithms or
statistical inference algorithms into deep neural networks. The ideas combining deep
learning and models are also emerging in other fields such as PDE, control, etc. We
will also summarize and present perspectives along this research direction.</p>},
issn = {2708-0579},
doi = {https://doi.org/10.4208/csiam-am.2020-0016},
url = {https://global-sci.com/article/82380/model-meets-deep-learning-in-image-inverse-problems}
}