Consistent Multi- and Single-View HDR-Image Reconstruction from Single Exposures
Consistent Multi- and Single-View HDR-Image Reconstruction from Single Exposures
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.
Aditya Mohan, Jing Zhang, Rémi Cozot, and Celine Loscos
Posters
CCS Concepts: Computing methodologies --> Computational photography; Machine learning; 3D imaging, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Computational photography, 3D imaging, CCS Concepts • Computing methodologies → Computational photography, Machine learning, Computing methodologies
CCS Concepts: Computing methodologies --> Computational photography; Machine learning; 3D imaging, [INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV], Computational photography, 3D imaging, CCS Concepts • Computing methodologies → Computational photography, Machine learning, Computing methodologies
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