The Narrow-Angle Cameras (NACs) onboard the Lunar Reconnaissance Orbiter Camera (LROC) capture lunar images that play a crucial role in current lunar exploration missions. Among these images, those of the Moon's permanently shadowed regions (PSRs) are highly noisy, obscuring the lunar topographic features within these areas. While significant advancements have been made in denoising techniques based on deep learning, the direct acquisition of paired clean and noisy images from the PSRs of the Moon is costly, making dataset acquisition expensive and hindering network training. To address this issue, we employ a physical noise model based on the imaging principles of the LROC NACs to generate noisy pairs of images for the Moon's PSRs, simulating realistic lunar imagery. Furthermore, inspired by the ideas of full-scale skip connections and self-attention models (Transformers), we propose a denoising method based on deep information convolutional neural networks. Using a dataset synthesized through the physical noise model, we conduct a comparative analysis between the proposed method and existing state-of-the-art denoising approaches. The experimental results demonstrate that the proposed method can effectively recover topographic features obscured by noise, achieving the highest quantitative metrics and superior visual results.
