Dual Exposure Fusion with Entropy-based Residual Filtering

作者：Heo Yong Seok; Lee Soochahn; Jung Ho Yub^*

来源：KSII Transactions on Internet and Information Systems, 2017, 11(5): 2555-2575.

DOI：10.3837/tiis.2017.05.014

• Exposure Fusion
• Image Enhancement
• Patch Match
• Residual Filtering

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 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.

• 出版日期2017-5-31