Reconfigurable Snapshot HDR Imaging Using Coded Masks

  • Masheal M. Alghamdi

Student thesis: Doctoral Thesis

Abstract

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
Date of AwardJul 10 2021
Original languageEnglish (US)
Awarding Institution
  • Computer, Electrical and Mathematical Science and Engineering
SupervisorWolfgang Heidrich (Supervisor)

Keywords

  • computational photography
  • high dynamic range
  • your thesis or dissertation: , , , , , . Student signature/date: Masheal M. Alghamdi 123808 2616 Circular Highway - Alwaha Unit # 1 Jeddah 23351-7154 Kingdom of Saudi Arabia Masheal.alghamdi@kaust.edu.sa July/2021 CS Ph.D. 10-Jul-2021 computational photography high dynamic range deep learning

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