Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation

Hanbyol Jang, Kihun Bang, Jinseong Jang, Dosik Hwang

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


In modern digital photographs, most images have low dynamic range (LDR) formats, which means that the range of light intensities from the darkest to the brightest is much lower than the range that can be perceived by the human eye. Therefore, to visualize images as naturally as possible on devices that display them in high dynamic range (HDR) format, the LDR images need to be converted into HDR images. The aim of this study was to develop an adaptive inverse tone mapping operator (iTMO) that can convert a single LDR image into a realistic HDR image based on artificial neural networks. In contrast to conventional iTMO algorithms, our technique was developed by learning the complicated relationship between various LDR-HDR pair images, which enabled nearly ground-truth HDR images to be generated from various types of LDR images. The novel learning technique is called cumulative histogram-based learning and color difference learning. The superior performance of our technique over conventional methods was assessed through objective evaluations of various types of LDR and HDR images.

Original languageEnglish
Article number9007464
Pages (from-to)38554-38567
Number of pages14
JournalIEEE Access
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was partially supported by Samsung Electronics Co.

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


Dive into the research topics of 'Dynamic Range Expansion Using Cumulative Histogram Learning for High Dynamic Range Image Generation'. Together they form a unique fingerprint.

Cite this