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Related Experiment Video

Updated: Oct 30, 2025

Digital Inline Holographic Microscopy DIHM of Weakly-scattering Subjects

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Published on: February 8, 2014

Deep HDR Hallucination for Inverse Tone Mapping.

Demetris Marnerides¹, Thomas Bashford-Rogers², Kurt Debattista¹

¹WMG, University of Warwick, Coventry CV4 7AL, UK.

Sensors (Basel, Switzerland)

|July 2, 2021

Summary

This study introduces a Generative Adversarial Network (GAN) method for Inverse Tone Mapping (ITM) to recover High Dynamic Range (HDR) details from Low Dynamic Range (LDR) images. The GAN approach significantly improves hallucinating missing image information, outperforming existing methods.

Keywords:

deep learning high dynamic range inverse tone mapping

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Last Updated: Oct 30, 2025

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Area of Science:

Computer Vision
Image Processing
Artificial Intelligence

Background:

Inverse Tone Mapping (ITM) aims to reconstruct High Dynamic Range (HDR) from Low Dynamic Range (LDR) images.
Current ITM methods struggle with recovering (hallucinating) missing information in over/under-exposed image regions.
Generative Adversarial Networks (GANs) show potential for improving image synthesis and inpainting tasks.

Purpose of the Study:

To develop and evaluate a GAN-based method for hallucinating missing HDR information in LDR images.
To compare the proposed GAN method against existing ITM techniques.
To introduce novel normalization and data augmentation methods for HDR hallucination.

Main Methods:

A novel Generative Adversarial Network (GAN) architecture is proposed for hallucinating missing HDR information.
The method is compared quantitatively against state-of-the-art Inverse Tone Mapping techniques.
A density-based normalization and an HDR data augmentation technique are introduced.

Main Results:

The proposed GAN-based ITM method achieves competitive quantitative results compared to current state-of-the-art.
The method effectively expands dynamic range in well-exposed areas.
Plausible hallucinations are generated for saturated and under-exposed regions.

Conclusions:

GANs offer a promising approach to enhance the quality of hallucinated HDR information in ITM.
The proposed method provides a robust solution for both dynamic range expansion and information recovery.
The introduced normalization and augmentation techniques support HDR hallucination tasks.