Learning High Dynamic Range from Outdoor Panoramas
arXiv:1703.10200
Abstract
Outdoor lighting has extremely high dynamic range. This makes the process of capturing outdoor environment maps notoriously challenging since special equipment must be used. In this work, we propose an alternative approach. We first capture lighting with a regular, LDR omnidirectional camera, and aim to recover the HDR after the fact via a novel, learning-based inverse tonemapping method. We propose a deep autoencoder framework which regresses linear, high dynamic range data from non-linear, saturated, low dynamic range panoramas. We validate our method through a wide set of experiments on synthetic data, as well as on a novel dataset of real photographs with ground truth. Our approach finds applications in a variety of settings, ranging from outdoor light capture to image matching.
8 pages + 2 pages of citations, 10 figures. Accepted as an oral paper at ICCV 2017
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Cited by in corpus (6)
- HDR image reconstruction from a single exposure using deep CNNs
- Deep Chain HDRI: Reconstructing a High Dynamic Range Image from a Single Low Dynamic Range Image
- Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications
- Image Correction via Deep Reciprocating HDR Transformation
- Switchable Temporal Propagation Network
- Spectrally Consistent UNet for High Fidelity Image Transformations