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Low-light image enhancement is a necessary preprocessing step for target detection and recognition in low-light environment and urgently needed in night vision monitoring, medical imaging, remote sensing imaging and other fields. With the rapid development of machine learning research, machine learning based low-light image enhancement has attracted extensive attention and achieved good results. However, most of the existing machine learning based low-light image enhancement methods rely on the "bright-dark" paired datasets. On the one hand, the construction of the paired dataset has a high cost, which is not conducive to the promotion and practical application. On the other hand, in practical problems, we can usually get partly-paired images with similar background, and there are a lot of shared information between these images. Taking full advantage of this shared information is also conducive to further improve the efficiency of learning methods. This paper focuses on the low-light image enhancement model based on Retinex theory. By mining and modeling the shared prior between partly-paired images of the same scene, and coupling with the existing machine learning methods based on paired dataset training, a Retinex model for partly-paired low-light image enhancement method with learned prior is proposed. Experiments demonstrate that the proposed method can recover more details and richer colors in visual effects, and can improve the numerical results by up to 20%.
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