Research On CE-1Satellite Lunar Surface Image Super-Resolution

Chang e1(CE-1) satellite CCD stereo camera has completed athree-dimensional imaging on the moon, and obtained great images of the lunarsurface. In order to further improve the spatial resolution of the lunar images toachieve excavating the CE-1lunar image data, we need to carry out super-resolutionresearch of the images. Meanwhile, this research will also lay the foundation for CE-2satellite super-resolution recovery. The main contents of this paper include: themeasurement of CE-1satellite CCD camera point spread function, thesuper-resolution algorithm for lunar surface images, and the evaluation of thesuper-resolution results.To begin with, after a detailed analysis of the CE-1satellite lunar datacharacteristics, combined with the super-resolution theoretical foundation andalgorithm, the super-resolution recovery model applied to the lunar images isobtained.Then, we introduce the measurement process of the super-resolution prioriparameters in depth, in which we mainly complete measuring point spread function ofCCD camera, including the purpose of the experiment, experimental principle, designand implementation and experimental data processing. Among them, the core idea ofexperimental principle is over-sampling the image plane by the way of sub-pixeldisplacement, and then, we get the two-dimensional point spread function by fitting.Finally, taking the relative motion between the satellite and the lunar surface intoaccount, we modify the obtained point spread function with the motion blur, and getthe final point spread function. In the end, we select the Maximum A Posteriori method (MAP) as thesuper-resolution algorithm of the recovery. Then, we introduce the instantiated modeland the solving method of the objective function in detail. Among them, the Gaussmodel and the Gibbs model are chose to instantiate the objective function and theconjugate gradient method is used to solve it. Next, simulated pairs are used to test theresilience and the application of the algorithm, and the results show that this algorithmcan be used for super-resolution recovery of the lunar images and can improve theresolution of more than1.36times. Finally, we show the super-resolution results, andevaluate them in terms of the subjective visual, objective indicators (entropy anddefinition), and the spectrum respectively, which show that the quality of thesuper-resolution images is great.