Lunar surface image restoration using U-net based deep neural networks
This work addresses image restoration for lunar surface data, but it is incremental as it applies an existing U-Net method to a new dataset.
The paper tackled the problem of restoring missing pixels in lunar surface images, known as image inpainting, using a U-Net based deep neural network, and achieved good visual quality and improved PSNR values.
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.