Deep Neural Network (DNN)-based image reconstruction, despite many successes, often exhibits uneven fidelity between high and low spatial frequency bands. In this paper we propose the Learning Synthesis by DNN (LS-DNN) approach where two DNNs process the low and high spatial frequencies, respectively, and, improving over [30], the two DNNs are trained separately and a third DNN combines them into an image with high fidelity at all bands. We demonstrate LS-DNN in two canonical inverse problems: super-resolution (SR) in diffraction-limited imaging (DLI), and quantitative phase retrieval (QPR). Our results also show comparable or improved performance over perceptual-loss based SR [21], and can be generalized to a wider range of image recovery problems.
@article{arxiv.1811.07945,
title = {Learning to synthesize: splitting and recombining low and high spatial frequencies for image recovery},
author = {Mo Deng and Shuai Li and George Barbastathis},
journal= {arXiv preprint arXiv:1811.07945},
year = {2018}
}
Comments
10 pages, 10 figures. Supplement file can be provided upon reasonable request