IMAGE PROCESSING, PATTERN RECOGNITION
Image restoration in diffractive optical systems using deep learning and deconvolution
A. V. Nikonorovab, M. V. Petrova, S. A. Bibikova, V. V. Kutikovaa, A. A. Morozovab, N. L. Kazanskiyba
a Samara National Research University, Samara, Russia
b Image Processing Systems Institute of the RAS - Branch of the FSRC "Crystallography and Photonics" RAS, Samara, Russia
In recent years, several pioneering works were dedicated to imaging systems based on simple diffractive structures like Fresnel lenses or phase zone plates. Such systems are much lighter and cheaper than classical refractive optical systems. However, the quality of images obtained by diffractive optics suffers from stronger distortions of various types. In this paper, we show that a combination of the high-precision lens design with post-capture computational reconstruction allows one to attain a much higher image quality. The proposed reconstruction procedure uses a sequence of color correction, deconvolution, and a feedforward deep learning neural network. An improvement both in lens manufacturing and in image processing may contribute to the emergence of ultra-lightweight imaging systems varying from cameras for nano- and picosatellites to surveillance systems.
harmonic lens, remote sensing, deconvolution, deep learning, PSF estimation, color correction.
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A. V. Nikonorov, M. V. Petrov, S. A. Bibikov, V. V. Kutikova, A. A. Morozov, N. L. Kazanskiy, “Image restoration in diffractive optical systems using deep learning and deconvolution”, Computer Optics, 41:6 (2017), 875–887
Citation in format AMSBIB
\by A.~V.~Nikonorov, M.~V.~Petrov, S.~A.~Bibikov, V.~V.~Kutikova, A.~A.~Morozov, N.~L.~Kazanskiy
\paper Image restoration in diffractive optical systems using deep learning and deconvolution
\jour Computer Optics
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