Imaging through glass diffusers using densely connected convolutional networks

• Main Authors: Li, Shuai (Contributor), Deng, Mo (Contributor), Lee, Justin Wu (Contributor), Sinha, Ayan T (Contributor), Barbastathis, George (Contributor)
• Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science (Contributor), Massachusetts Institute of Technology. Department of Biology (Contributor), Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Massachusetts Institute of Technology. Department of Physics (Contributor)
• Format: Article
• Language: English
• Published: Oxford University Press, 2019-03-19T13:10:21Z.
• Subjects: Article
• Online Access: Get fulltext

Computational imaging through scatter generally is accomplished by first characterizing the scattering medium so that its forward operator is obtained and then imposing additional priors in the form of regularizers on the reconstruction functional to improve the condition of the originally ill-posed inverse problem. In the functional, the forward operator and regularizer must be entered explicitly or parametrically (e.g., scattering matrices and dictionaries, respectively). However, the process of determining these representations is often incomplete, prone to errors, or infeasible. Recently, deep learning architectures have been proposed to instead learn both the forward operator and regularizer through examples. Here, we propose for the first time, to our knowledge, a convolutional neural network architecture called "IDiffNet" for the problem of imaging through diffuse media and demonstrate that IDiffNet has superior generalization capability through extensive tests with well-calibrated diffusers. We also introduce the negative Pearson correlation coefficient (NPCC) loss function for neural net training and show that the NPCC is more appropriate for spatially sparse objects and strong scattering conditions. Our results show that the convolutional architecture is robust to the choice of prior, as demonstrated by the use of multiple training and testing object databases, and capable of achieving higher space-bandwidth product reconstructions than previously reported.
Singapore-MIT Alliance
United States. Office of the Director of National Intelligence. Rapid Analysis of Various Emerging Nanoelectronics
United States. Department of Energy (DE-FG02-97ER25308)
United States. Department of Energy. Computational Science Graduate Fellowship Program