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Deep HDR hallucination for inverse tone mapping

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Marnerides, Demetris, Bashford-Rogers, Thomas and Debattista, Kurt (2021) Deep HDR hallucination for inverse tone mapping. Sensors, 21 (12). 4032. doi:10.3390/s21124032 ISSN 1424-8220.

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Official URL: http://dx.doi.org/10.3390/s21124032

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Abstract

Inverse Tone Mapping (ITM) methods attempt to reconstruct High Dynamic Range (HDR) information from Low Dynamic Range (LDR) image content. The dynamic range of well-exposed areas must be expanded and any missing information due to over/under-exposure must be recovered (hallucinated). The majority of methods focus on the former and are relatively successful, while most attempts on the latter are not of sufficient quality, even ones based on Convolutional Neural Networks (CNNs). A major factor for the reduced inpainting quality in some works is the choice of loss function. Work based on Generative Adversarial Networks (GANs) shows promising results for image synthesis and LDR inpainting, suggesting that GAN losses can improve inverse tone mapping results. This work presents a GAN-based method that hallucinates missing information from badly exposed areas in LDR images and compares its efficacy with alternative variations. The proposed method is quantitatively competitive with state-of-the-art inverse tone mapping methods, providing good dynamic range expansion for well-exposed areas and plausible hallucinations for saturated and under-exposed areas. A density-based normalisation method, targeted for HDR content, is also proposed, as well as an HDR data augmentation method targeted for HDR hallucination.

Item Type: Journal Article
Subjects: Q Science > Q Science (General)
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TR Photography
Divisions: Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group)
Library of Congress Subject Headings (LCSH): High dynamic range imaging, Image processing -- Digital techniques, Neural networks (Computer science), Machine learning
Journal or Publication Title: Sensors
Publisher: MDPI AG
ISSN: 1424-8220
Official Date: 11 June 2021
Dates:
DateEvent
11 June 2021Published
8 June 2021Accepted
Volume: 21
Number: 12
Article Number: 4032
DOI: 10.3390/s21124032
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 16 June 2021
Date of first compliant Open Access: 17 June 2021
Is Part Of: 1

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