Multispectral Joint Image Restoration via Optimizing a Scale Map2h
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通过 Pattern Analysis and Machine Intelligence, IEEE Transactions on - new TOC[url=][/url]




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.