Multi-Exposure Decomposition-Fusion Model for High Dynamic Range Image Saliency Detection

Xu WANG, Zhenhao SUN, Qiudan ZHANG, Yuming FANG, Lin MA, Shiqi WANG, Sam KWONG

Research output: Journal PublicationsJournal Article (refereed)peer-review

13 Citations (Scopus)


High dynamic range (HDR) imaging techniques have witnessed a great improvement in the past few decades. However, saliency detection task on HDR content is still far from well explored. In this paper, we introduce a multi-exposure decomposition-fusion model for HDR image saliency detection inspired by the brightness adaption mechanism. The proposed model is composed of three modules. Firstly, a decomposition module converts the input raw HDR image into a stack of LDR images by uniformly sampling the exposure time range. Secondly, a saliency region proposal network is employed to generate the candidate saliency maps for each LDR image in the exposure stack. Finally, an uncertainty weighting based fusion algorithm is applied to generate the overall saliency map for the input HDR image by merging the obtained LDR saliency maps. Extensive experiments show that our proposed model achieves superior performance compared with the state-of-theart methods on the existing HDR eye fixation databases. The source code of the proposed model are made publicly available at
Original languageEnglish
Pages (from-to)4409-4420
Number of pages12
JournalIEEE Transactions on Circuits and Systems for Video Technology
Issue number12
Early online date3 Apr 2020
Publication statusPublished - Dec 2020
Externally publishedYes

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 31670553, Grant 61871270, Grant 61672443, Grant 61620106008, and Grant 61702335, in part by the Natural Science Foundation of SZU under Grant 827000144, and in part by the National Engineering Laboratory for Big Data System Computing Technology of China.


  • brightness adaptation
  • deep learning
  • High dynamic range
  • image saliency detection


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