High Dynamic Range (HDR) image acquisition from a single image capture, also
known as snapshot HDR imaging, is challenging because the bit depths of camera
sensors are far from sufficient to cover the full dynamic range of the scene. Existing
HDR techniques focus either on algorithmic reconstruction or hardware modification
to extend the dynamic range. In this thesis, we propose a joint design for snapshot
HDR imaging by devising a spatially varying modulation mask in the hardware
combined with a deep learning algorithm to reconstruct the HDR image.
In this approach, we achieve a reconfigurable HDR camera design that does not
require custom sensors, and instead can be reconfigured between HDR and conventional
mode with very simple calibration steps. We demonstrate that the proposed
hardware-software solution offers a flexible, yet robust, way to modulate per-pixel
exposures, and the network requires little knowledge of the hardware to faithfully
reconstruct the HDR image. Comparative analysis demonstrated that our method
outperforms the state-of-the-art in terms of visual perception quality.
We leverage transfer learning to overcome the lack of sufficiently large HDR
datasets available. We show how transferring from a different large scale task (image
classification on ImageNet) leads to considerable improvements in HDR reconstruction
| Identifer | oai:union.ndltd.org:kaust.edu.sa/oai:repository.kaust.edu.sa:10754/670095 |
| Date | 10 July 2021 |
| Creators | Alghamdi, Masheal M. |
| Contributors | Heidrich, Wolfgang, Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division, Wonka, Peter, Ghanem, Bernard, Myszkowski, Karol |
| Source Sets | King Abdullah University of Science and Technology |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
Page generated in 0.0017 seconds