Semantic Pre-supplement for Exposure Correction

Zhen Zou, Wei Yu, Jie Huang, Feng Zhao; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5961-5970

Exposure correction tasks are dedicated to recovering the brightness and structural information of overexposed or underexposed images. The recovery difficulty of areas with different exposure levels is different as severely exposed areas are more difficult to recover due to severe structural information loss than commonly exposed areas. However existing methods focus on the simultaneous recovery of global brightness and structure ignoring that the recovery difficulty varies between areas. To address this issue we propose a novel exposure correction strategy named "Inpainting Assisted Exposure Correction"(IAEC) which pre-performs image structure repair on severely exposed areas to guide the exposure correction process. This method is based on the observation that the contextual semantic information contained in the image structure can effectively help the overall image recovery and the lack of contextual semantic information in severely incorrectly exposed areas is very severe. The pre-performed structural repair by the inpainting model can well supplement the insufficient contextual semantic information caused by severe exposure. Therefore we use an inpainting model to perform pre-structure repair on severely exposed areas to obtain supplementary contextual semantic information and then align the structure-repaired image with the improperly exposed input at the feature level. Extensive experiments demonstrate that our method gets superior results than the state-of-the-art methods and has the potential to be applied to other tasks with similar context loss problems.