Imaging through glass diffusers using densely connected convolutional networks

Abstract

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 so as 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 found that the Negative Pearson Correlation Coefficient loss function for training 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.

References in corpus (7)

• Non-invasive real-time imaging through scattering layers and around corners via speckle correlations
• Phase recovery and holographic image reconstruction using deep learning in neural networks
• Image Transmission Through an Opaque Material
• Deep Learning Microscopy
• Convolutional neural networks that teach microscopes how to image
• Deep Convolutional Denoising of Low-Light Images
• Exploit imaging through opaque wall via deep learning