期刊
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
卷 37, 期 12, 页码 2518-2530出版社
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2015.2417569
关键词
Image restoration; image denoise; joint filtering; shadow detection; multispectral image; depth enhancement
资金
- Research Grants Council of the Hong Kong Special Administrative Region [412911]
Color, infrared and flash images captured in different fields can be employed to effectively eliminate noise and other visual artifacts. We propose a two-image restoration framework considering input images from different fields, for example, one noisy color image and one dark-flashed near-infrared image. The major issue in such a framework is to handle all structure divergence and find commonly usable edges and smooth transitions for visually plausible image reconstruction. We introduce a novel scale map as a competent representation to explicitly model derivative-level confidence and propose new functions and a numerical solver to effectively infer it following our important structural observations. Multispectral shadow detection is also used to make our system more robust. Our method is general and shows a principled way to solve multispectral restoration problems.
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