Deep gradual flash fusion for low-light enhancement

Jae Woo Kim, Je Ho Ryu, Jong Ok Kim

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper, we propose gradual flash fusion, a new imaging concept that enables acquisition of pseudo multi-exposure images in a passive manner. This means that our gradual flash capture does not require any user-side manipulation (taking multiple shots or varying camera settings). Continuous high-speed capture naturally contains different intensities of flash in a single shooting. The captured gradual flash images, containing different information of the same scene, are fused to generate higher-quality images, especially in a low light scenario. For gradual flash fusion, we use a Generative Adversarial Network (GAN) based approach, where the generator is a tailored convolutional Auto-Encoder for image fusion. For the training, we build a custom dataset comprising gradual flash images and corresponding ground truths. This enables supervised learning, unlike most conventional image fusion studies. Experimental results demonstrate that gradual flash fusion achieves artifact-free and noise-free results resembling ground truth, owing to supervised adversarial fusion.

Original languageEnglish
Article number102903
JournalJournal of Visual Communication and Image Representation
Volume72
DOIs
Publication statusPublished - 2020 Oct

Keywords

  • Auto-encoder
  • Flash fusion
  • GAN
  • Image fusion
  • Low light enhancement
  • Pseudo multi-exposure

ASJC Scopus subject areas

  • Signal Processing
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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