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 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.

Permanently shadowed regions (PSRs) at the lunar poles pique scientific interest on account of their cold trapping of volatiles that is highly relevant in the current scope of lunar exploration. Interiors of PSRs are largely unknown due to the challenging illumination conditions. In this letter, we describe a method for synthesizing images at PSRs based on the knowledge of incident solar illumination geometry and local topography that reflects light into PSRs.

Water ice and other volatile compounds may be present on the Moon's surface within permanently shadowed regions (PSRs) near the lunar poles. Understanding the composition, quantity, distribution, and form of water and other volatiles associated with lunar PSRs is identified as a Strategic Knowledge Gap (SKG) for NASA's human exploration program, projected to visit the lunar south pole in the next decade. These polar volatile deposits are also scientifically interesting, having potential to reveal important information about the delivery of water to the Earth-Moon system.