APSIPA Transactions on Signal and Information Processing > Vol 7 > Issue 1

Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion

Yuma Kinoshita, Tokyo Metropolitan University, Japan, Hitoshi Kiya, Tokyo Metropolitan University, Japan, kiya@tmu.ac.jp
 
Suggested Citation
Yuma Kinoshita and Hitoshi Kiya (2019), "Automatic exposure compensation using an image segmentation method for single-image-based multi-exposure fusion", APSIPA Transactions on Signal and Information Processing: Vol. 7: No. 1, e22. http://dx.doi.org/10.1017/ATSIP.2018.26

Publication Date: 02 Jan 2019
© 2018 Yuma Kinoshita and Hitoshi Kiya
 
Subjects
 
Keywords
Image enhancementImage segmentationMulti-exposure fusionExposure compensation
 

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This is published under the terms of the Creative Commons Attribution licence.

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In this article:
I. INTRODUCTION 
II. BACKGROUND 
III. PROPOSED IMAGE SEGMENTATION AND EXPOSURE COMPENSATION 
IV. SIMULATION 
V. CONCLUSION 

Abstract

In this paper, an automatic exposure compensation method is proposed for image enhancement. For the exposure compensation, a novel image segmentation method based on luminance distribution is also proposed. Most single-image-enhancement methods often cause details to be lost in bright areas in images or cannot sufficiently enhance contrasts in dark regions. The image-enhancement method that uses the proposed compensation method enables us to produce high-quality images which well represent both bright and dark areas by fusing pseudo multi-exposure images generated from a single image. Here, pseudo multi-exposure images are automatically generated by the proposed exposure compensation method. To generate effective pseudo multi-exposure images, the proposed segmentation method is utilized for automatic parameter setting in the compensation method. In experiments, image enhancement with the proposed compensation method outperforms state-of-the-art image enhancement methods including Retinex-based methods, in terms of both entropy and statistical naturalness. Moreover, visual comparison results show that the proposed compensation method is effective in producing images that clearly present both bright and dark areas.

DOI:10.1017/ATSIP.2018.26