Multi-exposure fusion with CNN features

Hui Li, Lei Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)

Abstract

Multi-exposure fusion (MEF) is a widely used approach to high dynamic range imaging. The selection of features for fusion weight calculation is important to the performance of MEF. In this paper, we investigate the effectiveness of convolutional neural network (CNN) features for MEF. Considering the fact that there are no ground-truth images in MEF to train an end-to-end CNN, we adopt the pre-trained networks in other tasks to extract the feature. Both the selection of network and the selection of convolution layer are studied. With the extracted CNN feature map, we compute the local visibility and consistency maps to determine the weight map for MEF. The proposed method works well for both static and dynamic scenes. It exhibits competitive quantitative measures, and presents perceptually pleasing MEF outputs with little halo effects.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Image Processing, ICIP 2018 - Proceedings
PublisherIEEE Computer Society
Pages1723-1727
Number of pages5
ISBN (Electronic)9781479970612
DOIs
Publication statusPublished - 29 Aug 2018
Event25th IEEE International Conference on Image Processing, ICIP 2018 - Athens, Greece
Duration: 7 Oct 201810 Oct 2018

Publication series

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

Conference

Conference25th IEEE International Conference on Image Processing, ICIP 2018
CountryGreece
CityAthens
Period7/10/1810/10/18

Keywords

  • CNN feature
  • High dynamic range imaging
  • Multi-exposure fusion

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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