Enhancing Lunar Reconnaissance Orbiter Images via Multi-Frame Super Resolution for Future Robotic Space Missions

This paper presents a novel application of a Multi-frame Super Resolution (MFSR) method for lunar surface imagery called Lunar HighRes-net (L-HRN). In this work, we adapted and used NASA's Lunar Reconnaissance Orbiter (LRO) image database to train the Deep Learning architecture for image super resolution. Additionally, we also gathered an artificial image dataset from our virtual Moon to improve the amount of input data in the neural network training process. The network's architecture follows a standard MFSR algorithm that was enhanced for this specific use case. The proposed MFSR method has been evaluated using the well-known peak signal-to-noise ratio (PSNR) metric against other generic super-resolution methods of the state of the art. This work aims to improve environmental knowledge about the lunar surface to enhance future autonomous robots capabilities on the surface of the Moon.