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Near-Infrared Coloring via a Contrast-Preserving Mapping Model
Near-infrared gray images captured together with corresponding visible c...
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Near-Infrared Image Dehazing Via Color Regularization
Near-infrared imaging can capture haze-free near-infrared gray images an...
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A Novel Image Dehazing and Assessment Method
Images captured in hazy weather conditions often suffer from color contr...
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Image Fusion Using LEP Filtering and Bilinear Interpolation
Image Fusion is the process in which core information from a set of comp...
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A New Approach of Improving CFA Image for Digital Camera's
This paper work directly towards the improving the quality of the image ...
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Superpixel-based Color Transfer
In this work, we propose a fast superpixel-based color transfer method (...
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Variational Osmosis for Non-linear Image Fusion
We propose a new variational model for nonlinear image fusion. Our appro...
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Multimodal Fusion via a Series of Transfers for Noise Removal
Near-infrared imaging has been considered as a solution to provide high quality photographs in dim lighting conditions. This imaging system captures two types of multimodal images: one is near-infrared gray image (NGI) and the other is the visible color image (VCI). NGI is noise-free but it is grayscale, whereas the VCI has colors but it contains noise. Moreover, there exist serious edge and brightness discrepancies between NGI and VCI. To deal with this problem, a new transfer-based fusion method is proposed for noise removal. Different from conventional fusion approaches, the proposed method conducts a series of transfers: contrast, detail, and color transfers. First, the proposed contrast and detail transfers aim at solving the serious discrepancy problem, thereby creating a new noise-free and detail-preserving NGI. Second, the proposed color transfer models the unknown colors from the denoised VCI via a linear transform, and then transfers natural-looking colors into the newly generated NGI. Experimental results show that the proposed transfer-based fusion method is highly successful in solving the discrepancy problem, thereby describing edges and textures clearly as well as removing noise completely on the fused images. Most of all, the proposed method is superior to conventional fusion methods and guided filtering, and even the state-of-the-art fusion methods based on scale map and layer decomposition.
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