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Night images suffer not only from low light, but also from unevendistributions of light. Most existing night visibility enhancement methodsfocus mainly on enhancing low-light regions. This inevitably leads to overenhancement and saturation in bright regions, such as those regions affected bylight effects (glare, floodlight, etc). To address this problem, we need tosuppress the light effects in bright regions while, at the same time, boostingthe intensity of dark regions. With this idea in mind, we introduce anunsupervised method that integrates a layer decomposition network and alight-effects suppression network. Given a single night image as input, ourdecomposition network learns to decompose shading, reflectance andlight-effects layers, guided by unsupervised layer-specific prior losses. Ourlight-effects suppression network further suppresses the light effects and, atthe same time, enhances the illumination in dark regions. This light-effectssuppression network exploits the estimated light-effects layer as the guidanceto focus on the light-effects regions. To recover the background details andreduce hallucination/artefacts, we propose structure and high-frequencyconsistency losses. Our quantitative and qualitative evaluations on real imagesshow that our method outperforms state-of-the-art methods in suppressing nightlight effects and boosting the intensity of dark regions.

Unsupervised Night Image Enhancement: When Layer Decomposition Meets Light-Effects Suppression