Adaptive Brightness Enhancement Algorithm of Small Celestial Bodies Based on Knowledge Embedded

The economic and scientific value that small celestial bodies (SCBs) offer humanity is the main motivation for close exploration of these bodies. However, autonomous optical navigation is challenging due to the light variation caused by the rapid spin of SCBs. In this context, we propose a light prior brightness equalization self-calibration method, which can achieve brightness equalization of SCB images under varying illumination conditions while preserving image details, thereby increasing the number of feature-matching points. First, we design a light prior information function based on the illumination variation law of Lambert’s cosine law. Based on the function, the high-light and low-light areas of SCB images are distinguished. Furthermore, we create a brightness equalization mathematical model that maps the illumination components of high-light and low-light areas. Then, based on the brightness equalization mathematical model, we construct a light prior brightness self-calibration network. The proposed network includes 3 main modules: the illumination component estimation module, brightness self-calibration module, and light prior information prediction module; the proposed network utilizes a multistage illumination sharing approach to achieve separation and optimization of illumination components. Finally, the experimental results show that our method can achieve brightness equalization, markedly increasing the number of correct feature matches.