Dual Exposure Fusion with Entropy-based Residual Filtering
- Authors
- Heo, Yong Seok; Lee, Soochahn; Jung, Ho Yub
- Issue Date
- 31-May-2017
- Publisher
- 한국인터넷정보학회
- Keywords
- Exposure Fusion; Image Enhancement; Patch Match; Residual Filtering
- Citation
- KSII Transactions on Internet and Information Systems, v.11, no.5, pp 2555 - 2575
- Pages
- 21
- Journal Title
- KSII Transactions on Internet and Information Systems
- Volume
- 11
- Number
- 5
- Start Page
- 2555
- End Page
- 2575
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/7552
- DOI
- 10.3837/tiis.2017.05.014
- ISSN
- 1976-7277
1976-7277
- Abstract
- This paper presents a dual exposure fusion method for image enhancement. Images taken with a short exposure time usually contain a sharp structure, but they are dark and are prone to be contaminated by noise. In contrast, long-exposure images are bright and noise-free, but usually suffer from blurring artifacts. Thus, we fuse the dual exposures to generate an enhanced image that is well-exposed, noise-free, and blur-free. To this end, we present a new scale-space patch-match method to find correspondences between the short and long exposures so that proper color components can be combined within a proposed dual non-local (DNL) means framework. We also present a residual filtering method that eliminates the structure component in the estimated noise image in order to obtain a sharper and further enhanced image. To this end, the entropy is utilized to determine the proper size of the filtering window. Experimental results show that our method generates ghost-free, noise-free, and blur-free enhanced images from the short and long exposure pairs for various dynamic scenes.
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Collections - College of Engineering > Department of Electronic Engineering > 1. Journal Articles
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