research-article

HDR-cGAN: single LDR to HDR image translation using conditional GAN

Authors:

Prarabdh Raipurkar,

Shanmuganathan RamanAuthors Info & Claims

ICVGIP '21: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 17, Pages 1 - 9

https://doi.org/10.1145/3490035.3490275

Published: 19 December 2021 Publication History

Abstract

The prime goal of digital imaging techniques is to reproduce the realistic appearance of a scene. Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. The captured images turn out to be either too dark (underexposed) or too bright (overexposed). Specifically, saturation in overexposed regions makes the task of reconstructing a High Dynamic Range (HDR) image from single LDR image challenging. In this paper, we propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We formulate this problem as an image-to-image (I2I) translation task. To this end, we present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNTH datasets. Our framework uses an overexposed mask obtained from a pre-trained segmentation model to facilitate the hallucination task of adding details in the saturated regions. We demonstrate the effectiveness of the proposed method by performing an extensive quantitative and qualitative comparison with several state-of-the-art single-image HDR reconstruction techniques.

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Index Terms

HDR-cGAN: single LDR to HDR image translation using conditional GAN
1. Computing methodologies
  1. Computer graphics
    1. Image manipulation
      1. Computational photography
      2. Image processing

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cover image ACM Other conferences

ICVGIP '21: Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing

December 2021

428 pages

ISBN:9781450375962

DOI:10.1145/3490035

General Chairs:
Rama Chellappa
Johns Hopkins University
,
Santanu Chaudhury
IIT Jodhpur
,
Program Chairs:
Chetan Arora
IIT Delhi
,
Parag Chaudhuri
IIT Bombay
,
Subhransu Maji
University of Massachusetts, Amherst

Copyright © 2021 ACM.

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Published: 19 December 2021

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ICVGIP '21

ICVGIP '21: Indian Conference on Computer Vision, Graphics and Image Processing

December 19 - 22, 2021

Jodhpur, India

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Barua HMg TPramanick PSarkar C(2024)Enabling Social Robots to Perceive and Join Socially Interacting Groups using F-formation: A Comprehensive OverviewACM Transactions on Human-Robot Interaction10.1145/3682072Online publication date: 29-Jul-2024
https://dl.acm.org/doi/10.1145/3682072
Nayak AVenugopala PAshwini B(2024)A Systematic Review on Generative Adversarial Network (GAN): Challenges and Future DirectionsArchives of Computational Methods in Engineering10.1007/s11831-024-10119-1Online publication date: 14-May-2024
https://doi.org/10.1007/s11831-024-10119-1
Di Maro AAzpiroz IBiain XLongo GOlaizola I(2023)Overcoming Adverse Conditions in Rescue Scenarios: A Deep Learning and Image Processing ApproachApplied Sciences10.3390/app1309549913:9(5499)Online publication date: 28-Apr-2023
https://doi.org/10.3390/app13095499
Dalal DVashishtha GSingh PRaman S(2023)Single Image LDR to HDR Conversion Using Conditional Diffusion2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222821(3533-3537)Online publication date: 8-Oct-2023
https://doi.org/10.1109/ICIP49359.2023.10222821
Guo BLin C(2023)Single-Image HDR Reconstruction Based on Two-Stage GAN Structure2023 IEEE International Conference on Image Processing (ICIP)10.1109/ICIP49359.2023.10222156(91-95)Online publication date: 8-Oct-2023
https://doi.org/10.1109/ICIP49359.2023.10222156
Barua HKrishnasamy GWong KStefanov KDhall A(2023)ArtHDR-Net: Perceptually Realistic and Accurate HDR Content Creation2023 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.1109/APSIPAASC58517.2023.10317568(806-812)Online publication date: 31-Oct-2023
https://doi.org/10.1109/APSIPAASC58517.2023.10317568

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