Hdr Image Saliency Estimation by Convex Optimization

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we propose a convex optimization-based method for the visual saliency prediction of high dynamic range (HDR) images, which allows straightforward reuse of any existing saliency estimation methods for typical images with low dynamic range (LDR). First, the proposed method decomposes a given HDR image into multiple LDR images with different levels of intensity using a tone-mapping-based synthesis of imaginary multiexposure images. For each decomposed image, a standard saliency estimation method is then applied for typical LDR images. Finally, the saliency map of each decomposed image is integrated into a single map by solving convex optimization problems. The proposed method is applied to actual HDR images and its effectiveness is demonstrated.

Original languageEnglish
Title of host publication2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
PublisherIEEE Computer Society
Pages458-462
Number of pages5
ISBN (Electronic)9781728163956
DOIs
Publication statusPublished - Oct 2020
Event2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates
Duration: Sep 25 2020Sep 28 2020

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2020-October
ISSN (Print)1522-4880

Conference

Conference2020 IEEE International Conference on Image Processing, ICIP 2020
CountryUnited Arab Emirates
CityVirtual, Abu Dhabi
Period9/25/209/28/20

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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