학술논문

Toward Ultralightweight Remote Sensing With Harmonic Lenses and Convolutional Neural Networks
Document Type
Periodical
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of. 11(9):3338-3348 Sep, 2018
Subject
Geoscience
Signal Processing and Analysis
Power, Energy and Industry Applications
Lenses
Harmonic analysis
Optical diffraction
Optical imaging
Remote sensing
Image color analysis
Color correction
deconvolution
deep learning
harmonic lens
point spread function (PSF) estimation
remote sensing
Language
ISSN
1939-1404
2151-1535
Abstract
In this paper, we describe our advances in manufacturing a 256-layer 7- μ m thick harmonic lens with 150 and 300 mm focal distances combined with color correction, deconvolution, and a feedforwarding deep learning neural network capable of producing images approaching photographic visual quality. While reconstruction of images taken with diffractive optics was presented in previous works, this paper is the first to use deep neural networks during the restoration step. The level of imaging quality we achieved with our imaging system can facilitate the emergence of ultralightweight remote sensing cameras for nano- and pico-satellites, and for aerial remote sensing systems onboard small UAVs and solar-powered airplanes.