The permanently shadowed regions (PSRs) of the Moon are located at the Moon's polar regions that are permanently in shadow due to their inability to receive direct sunlight. Images of these areas are usually dark and significantly affected by noise, obscuring the lunar terrain information. Although image denoising has made considerable progress, there is still limited study on images denoising of lunar PSRs. The main challenge lies in the fact that images of PSRs are characterized by low contrast, complex noise type, and uneven illumination. The existing deep learning-based methods exhibit poor physical interpretability and cannot effectively remove complex noise. Therefore, this study introduces a novel denoising method by using combination of physical noise models and deep Learning. Specially, the physical noise model is used to simulate the noise of lunar PSRs according to the imaging principles of the lunar reconnaissance orbiter camera narrow angle camera. The improved deep learning model, which incorporates full-scale skip connections and Transformer is used to denoise the images. The proposed method is tested in 297 PRSs images with latitudes below -70 degrees and compared with state-of-the-art methods. Experimental results demonstrate that the proposed method outperforms existing methods in restoring terrain details and provides better quantitative and visual outcomes. This approach has the potential to improve the clarity of lunar PSR images and support future lunar exploration.

Lunar exploration has attracted considerable attention, with the lunar poles emerging as the next exploration hot spot for the cold trapping of volatiles in the permanently shadowed regions (PSRs) at these poles. Remote sensing via the satellite's optical load is one of the most important ways to get the scientific data of PSRs. However, the illumination conditions at the lunar poles are quite different from the low latitude areas and how to get appropriate optical signal remains challenging. Thus, simulation of the optical remote sensing process, which provides reference for the choice of satellites' imaging parameters to ensure the implementation of lunar exploration project, is of great value. In this article, an optical imaging chain modeling for the PSRs at the lunar south pole, which includes lunar 3-D topography, observing satellite's orbit, instrument's parameters, and other environmental parameters, has been built. To demonstrate the physical accuracy, some PSRs' observations acquired by narrow angle cameras (NACs) equipped on the lunar reconnaissance orbiter (LRO) are compared with the corresponding images simulated by the proposed imaging chain model. The digital value's difference between the simulated images and real captured images is generally less than 50 for 12-bit images ranging from 0 to 4095, indicating a good fit considering the uncertainty of soil's absolute reflectance and the noise in the real captured images. In addition, the impact of the imaging chain's parameters is revealed with the proposed algorithm. The simulation method will provide reference and assist future optical imaging of PSRs.

Lunar Reconnaissance Orbiter (LRO) was launched in 2009 to study and map the Moon and is now completing its fifth extended science mission. The LRO (see Figure 1) hosts a payload of seven different scientific instruments. The Cosmic Ray Telescope for the Effects of Radiation instrument has characterized the lunar radiation environment and allowed scientists to determine potential impacts to astronauts and other life. The Diviner Lunar Radiometer Experiment (DLRE) has identified cold traps where ice could reside and mapped global thermophysical and mineralogical properties by measuring surface and subsurface temperatures. The Lyman Alpha Mapping Project has found evidence of exposed ice in south polar cold traps as well as global diurnal variations in hydration. The Lunar Exploration Neutron Detector has been used to create high-resolution maps of lunar hydrogen distribution and gather information about the neutron component of the lunar radiation environment. The Lunar Reconnaissance Orbiter Camera (LROC) is a system of three cameras [one wide-angle camera and two narrow-angle cameras (NACs)] mounted on the LRO that capture high-resolution black-and-white images and moderate resolution multispectral (seven-color band) images of the lunar surface. These images can be used, for example, to learn new details about the history of lunar volcanism or the present-day flux of impactors. The Miniature Radio Frequency (Mini-RF) instrument is an advanced synthetic aperture radar (SAR) that can probe surface and subsurface coherent rock contents to identify the polarization signature of ice in cold traps. The Lunar Orbiter Laser Altimeter (LOLA) has been used to generate a high-resolution, 3D map of the Moon that serves as the most accurate geodetic framework available for co-locating LRO (and other lunar) data. The data produced by the LRO continue to revolutionize our scientific understanding of the Moon, and are essential to planning NASA's future human and robotic lunar missions.

This study demonstrates the visualization and recovery of the 3D dynamic trajectories of charged microparticles in electric field conditions. The main aim of this work is to simulate plasma-dust processes above the surfaces of the Moon and other Solar system bodies without atmospheres. The experimental setup includes two parts: a vacuum chamber, in which microparticles imitate lunar dust dynamics under electrostatic field conditions, and a stereo camera system for image registration combined with laser and optics for illuminating the investigation volume. Image processing techniques for estimating the 3D particle trajectory were developed. Examples of processing results and their prospective application are discussed.