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Title: Learned Patch-Based Regularization for Inverse Problems in Imaging
Many modern approaches to image reconstruction are based on learning a regularizer that implicitly encodes a prior over the space of images. For large-scale images common in imaging domains like remote sensing, medical imaging, astronomy, and others, learning the entire image prior requires an often-impractical amount of training data. This work describes a deep image patch-based regularization approach that can be incorporated into a variety of modern algorithms. Learning a regularizer amounts to learning the a prior for image patches, greatly reducing the dimension of the space to be learned and hence the sample complexity. Demonstrations in a remote sensing application illustrates that learning patch-based regularizers produces high-quality reconstructions and even permits learning from a single ground-truth image.  more » « less
Award ID(s):
1740707 1930049 1934637 1925101
NSF-PAR ID:
10183692
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)
Page Range / eLocation ID:
211 to 215
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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