Perceptual Evaluation for Multi-Exposure Image Fusion of Dynamic Scenes

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

3 Scopus Citations
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Author(s)

  • Yuming Fang
  • Hanwei Zhu
  • Kede Ma
  • Zhou Wang
  • Shutao Li

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number8839746
Pages (from-to)1127-1138
Journal / PublicationIEEE Transactions on Image Processing
Volume29
Online published16 Sep 2019
Publication statusPublished - 2020

Abstract

A common approach to high dynamic range (HDR) imaging is to capture multiple images of different exposures followed by multi-exposure image fusion (MEF) in either radiance or intensity domain. A predominant problem of this approach is the introduction of the ghosting artifacts in dynamic scenes with camera and object motion. While many MEF methods (often referred to as deghosting algorithms) have been proposed for reduced ghosting artifacts and improved visual quality, little work has been dedicated to perceptual evaluation of their deghosting results. Here we first construct a database that contains 20 multi-exposure sequences of dynamic scenes and their corresponding fused images by nine MEF algorithms. We then carry out a subjective experiment to evaluate fused image quality, and find that none of existing objective quality models for MEF provides accurate quality predictions. Motivated by this, we develop an objective quality model for MEF of dynamic scenes. Specifically, we divide the test image into static and dynamic regions, measure structural similarity between the image and the corresponding sequence in the two regions separately, and combine quality measurements of the two regions into an overall quality score. Experimental results show that the proposed method significantly outperforms the state-of-the-art. In addition, we demonstrate the promise of the proposed model in parameter tuning of MEF methods. The subjective database and the MATLAB code of the proposed model are made publicly available at https://github.com/h4nwei/MEF-SSIMd.

Research Area(s)

  • Heuristic algorithms, Dynamics, Databases, Image reconstruction, Quality assessment, Image fusion, Dynamic range, High dynamic range imaging, multi-exposure image fusion, ghosting, image quality assessment, structural similarity, INFORMATION

Citation Format(s)

Perceptual Evaluation for Multi-Exposure Image Fusion of Dynamic Scenes. / Fang, Yuming; Zhu, Hanwei; Ma, Kede; Wang, Zhou; Li, Shutao.

In: IEEE Transactions on Image Processing, Vol. 29, 8839746, 2020, p. 1127-1138.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal