Dual Exposure Fusion with Entropy-based Residual Filtering

• Yong Seok Heo,
• Soochahn Lee,
• Ho Yub Jung,

Vol. 11, No. 5, May 30, 2017
10.3837/tiis.2017.05.014, Download Paper (Free):

• Exposure Fusion
• image enhancement
• Patch Match
• Residual Filtering

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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