Browsing by Author "Mohan, Aditya"

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    Consistent Multi- and Single-View HDR-Image Reconstruction from Single Exposures
    (The Eurographics Association, 2022) Mohan, Aditya; Zhang, Jing; Cozot, Rémi; Loscos, Celine; Sauvage, Basile; Hasic-Telalovic, Jasminka
    We propose a CNN-based approach for reconstructing HDR images from just a single exposure. It predicts the saturated areas of LDR images and then blends the linearized input with the predicted outputs. Two loss functions are used: the Mean Absolute Error and the Multi-Scale Structural Similarity Index. The choice of these loss functions allows us to outperform previous algorithms in the reconstructed dynamic range. Once the network trained, we input multi-view images to it to output multi-view coherent images.
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    Consistent Multi- and Single-View HDR-Image Reconstruction from Single Exposures
    (The Eurographics Association, 2022) Mohan, Aditya; Zhang, Jing; Cozot, Remi; Loscos, Celine; Ronfard, Rémi; Wu, Hui-Yin
    Recently, 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.