Learning Exposure Correction in Dynamic Scenes

arXiv:2402.17296v1 Announce Type: new
Abstract: Capturing videos with wrong exposure usually produces unsatisfactory visual effects. While image exposure correction is a popular topic, the video counterpart is less explored in the literature. Directly applying prior image-based methods to input videos often results in temporal incoherence with low visual quality. Existing research in this area is also limited by the lack of high-quality benchmark datasets. To address these issues, we construct the first real-world paired video dataset, including both underexposure and …

abstract arxiv cs.cv dynamic effects image literature low popular prior quality research results temporal type video videos visual visual effects