Deep Reinforcement Learning for Lunar Polar Low-Light Enhancement

As a bridge between the moon and human perception, the lunar optical image reflects lunar topography, geology, and evolution. Unfortunately, the permanent shadow regions (PSRs) near the lunar poles suffer from information contamination due to insufficient illumination. Low-light enhancement is a subjective process whose target is tied to human visual perception. However, existing low-light enhancement methods often operate as opaque “black box”, lacking transparency and failing to accommodate diverse perceptual preferences. To this end, we explore a PSRs Low-Light Enhancer (PSRs-LLE) that treats low-light enhancement as a Markov decision process, thereby dynamically fitting perceptual preferences. Specifically, a deep Q network as an agent integrates multiple user-friendly attributes (e.g., brightness, contrast, chroma, and detail) through actions recursion (i.e., a candidate set of image enhancement operations). Such transparent and specific action sequences satisfy customization preferences of users while providing convincing interpretability, compared with the “black box” paradigm of deep learning. More importantly, a well-designed non-reference loss function liberates PSRs-LLE from the dilemma of virtual assumptions and paired data, which further enhances usability. Extensive experiments demonstrate that PSRs-LLE outperforms state-of-the-art methods in both qualitative and quantitative comparisons.