Deep-Space Background Low-Light Image Enhancement Method Based on Multi-Image Fusion

Existing low-light image enhancement methods often struggle to effectively enhance space targets in deep-space contexts due to the effects of extremely low illumination, stellar stray light, and Earth halos. This work proposes a low-light image enhancement method based on multi-image fusion, which integrates features of space targets with the Retinex theory. The method dynamically adjusts contrast by detecting luminance distribution and incorporates an adaptive noise removal mechanism for enhanced image quality. This method effectively balances detail enhancement with noise suppression. This work presents experiments on deep-space background images featuring 10 types of artificial satellites, including AcrimSat, Calipso, Jason, and others. Experimental results demonstrate that the proposed method outperforms traditional methods and mainstream deep learning models in qualitative and quantitative evaluations, particularly in suppressing Earth halo interference. This study establishes an effective framework for improving the visual quality of spacecraft images and provides important technical support for applications such as spacecraft identification, space target detection, and autonomous spacecraft navigation.