HDR video synthesis by a nonlocal regularization variational model

Abstract

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.