Abstract
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.
Abstract (translated)
图像恢复是一种从噪声观测中重建原始图像的可行估计的技术。本文提出了一种基于U-网的深度神经网络模型，以上下文感知的方式恢复月球表面图像上缺失的像素点，即图像修复问题。我们利用Kaguya卫星上的多波段成像仪（MI）拍摄的月球表面灰度图像进行了实验，结果表明，该方法能够重建出具有良好视觉质量和改善PSNR值的月球表面图像。
URL
https://arxiv.org/abs/1904.06683
