Real-world low-light image enhancement via domain-gap aware framework and reverse domain-distance guided strategy

Abstract

Low-light image enhancement (LLIE) has high practical value and development potential in real scenarios. However, the current LLIE methods reveal inferior generalization competence to real-world low-light (LL) conditions of poor visibility. We can attribute this phenomenon to the severe domain bias between the synthetic LL domain and the real-world LL domain. In this article, we put forward the Domain-Gap Aware Framework, a novel two-stage framework for real-world LLIE, which is the pioneering work to introduce domain adaptation into the LLIE. To be more specific, in the first stage, to eliminate the domain bias lying between the existing synthetic LL domain and the real-world LL domain, this work leverages the source domain images via adversarial training. By doing so, we can align the distribution of the synthetic LL domain to the real-world LL domain. In the second stage, we put forward the Reverse Domain-Distance Guided (RDDG) strategy, which takes full advantage of the domain-distance map obtained in the first stage and guides the network to be more attentive to the regions that are not compliance with the distribution of the real world. This strategy makes the network robust for input LL images, some areas of which may have large relative domain distances to the real world. Numerous experiments have demonstrated the efficacy and generalization capacity of the proposed method. We sincerely hope this analysis can boost the development of low-light domain research in different fields.