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Lunar surface image restoration using U-net based deep neural networks

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arxiv 1904.06683 v1 pith:XYBE27J7 submitted 2019-04-14 cs.CV

Hiya Roy , Subhajit Chaudhury , Toshihiko Yamasaki , Danielle DeLatte , Makiko Ohtake , Tatsuaki Hashimoto This is my paper · ORCID
classification cs.CV
keywords imagelunarsurfacedeepneuralrestorationu-netcaptured
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Image restoration is a technique that reconstructs a feasible estimate of the original image from the noisy observation. In this paper, we present a U-Net based deep neural network model to restore the missing pixels on the lunar surface image in a context-aware fashion, which is often known as image inpainting problem. We use the grayscale image of the lunar surface captured by Multiband Imager (MI) onboard Kaguya satellite for our experiments and the results show that our method can reconstruct the lunar surface image with good visual quality and improved PSNR values.

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  1. Deep Learning-Based Image Recovery and Pose Estimation for Resident Space Objects

    cs.CV 2025-01 conditional novelty 4.0 of 10

    On simulated blurred ISS imagery, U-Net-only restoration reduced pose-estimation angular error by about 72% relative to no preprocessing.

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