Learning Rank Constrained Exposure Correction from Unpaired Data

This paper presents a novel unpaired learning based exposure correction network that can robustly deal with both under- and over-exposed images. Given an input image in any exposure conditions, we first obtain its intermediate under- and over-exposure corrected versions by predicting dual illuminations for Retinex-based enhancement, the two outputs together with the original input are then fed to a multi-exposure fusion module to adaptively locate the best-exposed regions in the three images and then seamlessly fuse them into a well-exposed output. To ensure that the generated result is visually natural and free of disturbing visual artifacts such as loss of details and contrast degradation, we introduce a novel rank loss. Experiments show that our method outperforms existing methods in terms of both quantitative and qualitative results.