Mohan, AdityaZhang, JingCozot, RemiLoscos, CelineRonfard, RémiWu, Hui-Yin2022-04-202022-04-202022978-3-03868-173-12411-9733https://doi.org/10.2312/wiced.20221050https://diglib.eg.org:443/handle/10.2312/wiced20221050Recently, there have been attempts to obtain high-dynamic range (HDR) images from single exposures and efforts to reconstruct multi-view HDR images using multiple input exposures. However, there have not been any attempts to reconstruct multi-view HDR images from multi-view Single Exposures to the best of our knowledge. We present a two-step methodology to obtain color consistent multi-view HDR reconstructions from single-exposure multi-view low-dynamic-range (LDR) Images. We define a new combination of the Mean Absolute Error and Multi-Scale Structural Similarity Index loss functions to train a network to reconstruct an HDR image from an LDR one. Once trained we use this network to multi-view input. When tested on single images, the outputs achieve competitive results with the state-of-the-art. Quantitative and qualitative metrics applied to our results and to the state-of-the-art show that our HDR expansion is better than others while maintaining similar qualitative reconstruction results. We also demonstrate that applying this network on multi-view images ensures coherence throughout the generated grid of HDR images.CCS Concepts: Computing methodologies --> Computational photography; Machine learning; 3D imagingComputing methodologiesComputational photographyMachine learning3D imaging10.2312/wiced.2022105037-448 pages