HDR video synthesis by a nonlocal regularization variational model

• Published in: Journal of visual communication and image representation 2023-09, Vol.95, p.103883, Article 103883
• Main Authors: Buades, Antoni, Martorell, Onofre, Pereira-Sánchez, Ivan
• Format: Article
• Language: English
• Subjects: HDR video synthesis; Nonlocal regularization; Variational methods
• ISSN: 1047-3203; 1095-9076
• DOI: 10.1016/j.jvcir.2023.103883

High dynamic range (HDR) video synthesis is a very challenging task. Consecutive frames are acquired with alternate expositions, generally two or three different exposure times. Classical methods aim at registering neighboring frames and fuse them using image HDR techniques. However, the registration often fails to obtain accurate results and the fusion produces ghosting artifacts. Deep learning techniques have recently appeared imitating the structure of existing classical methods. The neural network is intended to estimate the registration function and choose the fusion weights. In this paper, we propose a new method for HDR video synthesis using a variational model. The proposed model uses a nonlocal regularization term to combine pixel information from neighboring frames. The obtained results are competitive with state-of-the-art. Moreover, the proposed method gives a more reliable and understandable solution than deep-learning based ones. •HDR video synthesis by using classical techniques.•We use a variational model with non-local regularization.•The proposed method obtains competitive results.•The proposed method is more reliable and understandable that deeplearning methods.