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http://dx.doi.org/10.3837/tiis.2017.05.014

Dual Exposure Fusion with Entropy-based Residual Filtering  

Heo, Yong Seok (Dept. of Electrical and Computer Engineering, Ajou University)
Lee, Soochahn (Dept. of Electrical Engineering, Soonchunhyang University)
Jung, Ho Yub (Dept. of Computer Engineering, Chosun University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.11, no.5, 2017 , pp. 2555-2575 More about this Journal
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.
Keywords
Exposure Fusion; Image Enhancement; Patch Match; Residual Filtering;
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Times Cited By KSCI : 3  (Citation Analysis)
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