Unsupervised HDR Image Reconstruction Based on Over/Under-Exposed LDR Image Pair
Hao Wang, Tao Zhang, Guoyu Lu · 2021
This paper proposes an unsupervised high dynamic range (HDR) image reconstruction method based on an over/under-exposed low dynamic range (LDR) image pair. The framework includes two end-to-end branches: transferring an over-exposed image input to under-exposed images and transferring an under-exposed image input to over-exposed images. The LDR images with the same exposure from the two branches are averaged, and then reconstruct an HDR image by merging them. When training the model, we use the L1loss of the same exposure image of the two branches and MEF-SSIM loss function as the objective function to ensure that the two branches get a similar visual effect at the same exposure, and use RGB loss and HSV loss to constrain the brightness and saturation of different exposure images. Experiments demonstrate that our unsupervised framework can generate comparable results with state-of-the-art supervised learning methods.