Higher spatial resolution imaging data is always desirable to the international community of planetary scientists interested in improving understanding of surface formation processes. We have previously developed a novel Super-resolution restoration (SRR) technique (Tao & Muller, 2016) using Gotcha sub-pixel matching, orthorectification, and segmented 4th order PDE-TV, called GPT SRR, which is able to restore 5 cm-12.5 cm near rover scale images (equivalent to Navcam projected FoV at a range of ≥ 5 m) from multiple 25 cm resolution NASA MRO HiRISE images. The SRR technique has been successfully applied to the rover traverses for the MER and MSL missions within the EU FP-7 PRoViDE project. These SRR results have revealed new surface information including the imaging of individual rocks (diameter ≥ 25 cm) by comparison of the original HiRISE image and rover Navcam orthorectified image mosaics. In this work, we seek evidence from processing a very large number of stereo reconstruction results from all Navcam stereo images within PRoViDE, registration and comparison with the corresponding SRR image, in order to derive a quantitative assessment on key features including rocks (diameter < 150 cm) and rover track wheel spacing. We summarise statistics from SRR-Navcam measurements and demonstrate that our unique SRR datasets will greatly support the geological and morphological analysis and monitoring of Martian surface and can also be applied to landing site selection, in order to avoid unsuitable terrain, for any future lander/rover as well as help to define future rover paths.
Heavy water: a simple solution to increasing the brightness of fluorescent proteins in super-resolution imaging
