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ExpandNet : a deep convolutional neural network for high dynamic range expansion from low dynamic range content
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Marnerides, Demetris, Bashford-Rogers, Thomas, Hatchett, Jonathan and Debattista, Kurt (2018) ExpandNet : a deep convolutional neural network for high dynamic range expansion from low dynamic range content. Computer Graphics Forum, 37 (2). pp. 37-49. doi:10.1111/cgf.13340 ISSN 0167-7055.
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Official URL: https://doi.org/10.1111/cgf.13340
Abstract
High dynamic range (HDR) imaging provides the capability of handling real world lighting as opposed to the traditional low dynamic range (LDR) which struggles to accurately represent images with higher dynamic range. However, most imaging content is still available only in LDR. This paper presents a method for generating HDR content from LDR content based on deep Convolutional Neural Networks (CNNs) termed ExpandNet. ExpandNet accepts LDR images as input and generates images with an expanded range in an end-to-end fashion. The model attempts to reconstruct missing information that was lost from the original signal due to quantization, clipping, tone mapping or gamma correction. The added information is reconstructed from learned features, as the network is trained in a supervised fashion using a dataset of HDR images. The approach is fully automatic and data driven; it does not require any heuristics or human expertise. ExpandNet uses a multiscale architecture which avoids the use of upsampling layers to improve image quality. The method performs well compared to expansion/inverse tone mapping operators quantitatively on multiple metrics, even for badly exposed inputs.
| Item Type: | Journal Article | |||||||||
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| Subjects: | Q Science > Q Science (General) T Technology > TR Photography |
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| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||
| Library of Congress Subject Headings (LCSH): | High dynamic range imaging, Image processing -- Digital techniques, Machine learning | |||||||||
| Journal or Publication Title: | Computer Graphics Forum | |||||||||
| Publisher: | Blackwell Publishing | |||||||||
| ISSN: | 0167-7055 | |||||||||
| Official Date: | 22 May 2018 | |||||||||
| Dates: |
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| Volume: | 37 | |||||||||
| Number: | 2 | |||||||||
| Page Range: | pp. 37-49 | |||||||||
| DOI: | 10.1111/cgf.13340 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | Published | |||||||||
| Reuse Statement (publisher, data, author rights): | "This is the peer reviewed version of the following article: Marnerides, D., Bashford‐Rogers, T. , Hatchett, J. and Debattista, K. (2018), ExpandNet: A Deep Convolutional Neural Network for High Dynamic Range Expansion from Low Dynamic Range Content. Computer Graphics Forum, 37: 37-49. doi:10.1111/cgf.13340, which has been published in final form at https://doi.org/10.1111/cgf.13340. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions." | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
| Date of first compliant deposit: | 7 March 2018 | |||||||||
| Date of first compliant Open Access: | 22 May 2019 | |||||||||
| RIOXX Funder/Project Grant: |
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