Hans-Werner Fink, Heinz Schmid, et al.
Journal of the Optical Society of America A: Optics and Image Science, and Vision
Physics-informed neutral networks (NN) are an emerging technique to improve spatial resolution and enforce physical consistency of data from physics models or satellite observations. A super-resolution (SR) technique is explored to reconstruct high resolution images (4x) from lower resolution images in an advection-diffusion model of atmospheric pollution plumes. SR performance is generally increased when the advection-diffusion equation constrains the NN in addition to conventional pixel-based constraints. The ability of SR techniques to also reconstruct missing data is investigated by randomly removing image pixels from the simulations and allowing the system to learn the content of missing data. Improvements in S/N of 11% are demonstrated when physics equations are included in SR with 40% pixel loss. Physics-informed NNs accurately reconstruct corrupted images and generate better results compared to the standard SR approaches.
Hans-Werner Fink, Heinz Schmid, et al.
Journal of the Optical Society of America A: Optics and Image Science, and Vision
Xiao-Yu Hu, Evangelos Eleftheriou, et al.
Israeli SYSTOR 2009
Balaji Ganesan, Srinivas Parkala, et al.
NeurIPS 2020
Xiaodan Song, Ching-Yung Lin, et al.
CVPRW 2004