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Author Patricia Suarez; Angel Sappa; Boris X. Vintimilla edit   pdf
doi  openurl
  Title Infrared Image Colorization based on a Triplet DCGAN Architecture Type Conference Article
  Year 2017 Publication IEEE Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time.
 
  Address Honolulu; Hawaii; USA; July 2017  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue (up) Edition  
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  Area Expedition Conference CVPRW  
  Notes ADAS; 600.086; 600.118 Approved no  
  Call Number Admin @ si @ SSV2017b Serial 2920  
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