Learnable Douglas-Rachford iteration and its applications in DOT imaging

DOI10.3934/IPI.2020031zbMath1457.94020OpenAlexW3023916593MaRDI QIDQ2188164

Nanguang Chen, Jiulong Liu, Hui Ji

Publication date: 4 June 2020

Published in: Inverse Problems and Imaging (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3934/ipi.2020031

zbMATH Keywords

inverse problemimage reconstructiondeep learningdiffusion optical tomographyoptimization unrolling

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Computing methodologies for image processing (68U10) Neural networks for/in biological studies, artificial life and related topics (92B20) Image processing (compression, reconstruction, etc.) in information and communication theory (94A08)

Uses Software

• U-Net
• Quicksilver
• Wasserstein GAN
• f-GAN

Cites Work

• Unnamed Item
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Nonlinear total variation based noise removal algorithms
• A three-operator splitting scheme and its optimization applications
• Data-driven tight frame construction and image denoising
• The Split Bregman Method for L1-Regularized Problems
• Splitting Algorithms for the Sum of Two Nonlinear Operators
• Solving ill-posed inverse problems using iterative deep neural networks
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Image restoration: Total variation, wavelet frames, and beyond

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