# Physics-Based Noise Modeling and Deep Learning for Denoising Permanently Shadowed Lunar Images
## 6 Conclusions  
This study expands the existing research on the denoising of lunar PSR images by combining physical noise models with deep learning techniques. The physical noise model is used to simulate the distribution and intensity of noise, and a training dataset is constructed based on this model. In addition, this article proposes an improved network architecture aimed at enhancing denoising capabilities. By testing simulated and actual images of the lunar PSR, the following conclusions were drawn:  
1. The physical noise model effectively simulated the noise in the lunar permanent shadow area images obtained from LROC NAC. More importantly, this model not only provides an effective solution for constructing training samples, but also enhances the physical interpretability of the network.
2. This study proposes a FRET network that combines full-scale skip connections with Transformer structures. By using full-scale skip connections and Transformer structures to enhance the network's ability to obtain multi-scale information and extract key information, the denoising effect on complex noisy images has been significantly improved.
3. This study proposes a REWin Transformer block consisting of SW-MSA, GFRN, and GCEN modules to remove redundant information from feature maps and reduce interference with network learning. Meanwhile, fine processing and local enhancement are applied to the remaining key information, further enhancing the network's ability to recover texture details from noisy images.  
Although the current model outperforms mainstream methods on both simulated and real datasets, some residual noise remains when handling strong stripe noise. Given the limitations of the current approach, improvements to the denoising method may involve integrating frequency-domain attention-based denoising techniques, particularly optimized for stripe noise, and developing noise estimation models to enhance the recognition of stripe features. Furthermore, in future research, we will focus on leveraging high-quality PSR images to explore geometric consistency constraints in multi-view PSR imagery, optimizing the accuracy of 3D point cloud reconstruction based on the texture details of the denoised results. Finally, by employing cross-modal feature alignment techniques, we aim to establish a multi-modal data fusion framework that integrates complementary information from Mini-RF radar data and optical imagery, thereby enhancing the spatial resolution of lunar PSR 3D reconstruction. This approach not only provides sub-meter precision 3D geographic information for lunar polar ice detection but also facilitates the transition of deep space exploration image processing from 2D to 3D.  
Conceptualization, H.P. and R.Z.; methodology, H.P., B.C. and R.Z.; software, B.C.; validation, H.P.; formal analysis, H.P.; investigation, B.C.; resources, R.Z.; data curation, B.C.; writing--original draft preparation, R.Z. and B.C.; writing--review and editing, R.Z., B.C. and H.P.; visualization, H.P.; supervision, R.Z. and H.P.; project administration, R.Z.; funding acquisition, R.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China under Grant 42241164.