Deep Learning Techniques for Inverse Problems in Imaging

By Greg Ongie, Ajil Jalal, Christopher A. Metzler, Richard Baraniuk, A. Dimakis, Rebecca M. Willett. IEEE Journal on Selected Areas in Information Theory, 2020

670 citations recorded by the source corpus.

Recent work in machine learning shows that deep neural networks can be used to solve a wide variety of inverse problems arising in computational imaging. We explore the central prevailing themes of this emerging area and present a taxonomy that can be used to categorize different problems and reconstruction methods. Our taxonomy is organized along two central axes: (1) whether or not a forward model is known and to what extent it is used in training and testing, and (2) whether or not the learning is supervised or unsupervised, i.e., whether or not the training relies on access to matched ground truth image and measurement pairs. We also discuss the tradeoffs associated with these different reconstruction approaches, caveats and common failure modes, plus open problems and avenues for future work.

Subfields: Biomedical Engineering, Computational Mechanics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b3e430ce15a956694d45203e69c537486025309f

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In Biomedical Engineering

In Computational Mechanics

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Denoising Diffusion Restoration Models (2022, background): “Various deep learning solutions have been suggested for solving inverse problems under different settings (see a detailed survey in [37]).”
Robust Compressed Sensing MRI with Deep Generative Priors (2021, methodology): “More recently, deep learning techniques have been used as powerful data-driven reconstruction methods for inverse problems [47, 64].”
Generative Diffusion Prior for Unified Image Restoration and Enhancement (2023, background): “2, 5, 8, 23 [60] Gregory Ongie, Ajil Jalal, Christopher A Metzler, Richard G Baraniuk, Alexandros G Dimakis, and Rebecca Willett.”
Deblurring via Stochastic Refinement (2021, background): “Training networks to go from corrupted images to a known ground truth in a supervised way belongs in the family of end-to-end methods [50].”
Transformers in Medical Imaging: A Survey (2022, methodology): “These hybrid methods contain special domain-specific layers, and include unrolled optimization [77], generative models [78], and learned denoiser-based approaches [79].”
Intermediate Layer Optimization for Inverse Problems using Deep Generative Models (2021, background): “Image reconstruction methods can be either supervised (Pathak et al., 2016; Richardson et al., 2020; Yu et al., 2018) or unsupervised (Menon et al., 2020; Bora et al., 2017; Pajot et al., 2019), see the recent survey (Ongie et al., 2020) for a uniﬁed presentation.”