A Fast Near-Infrared Image Colorization Deep Learning Mode


  • ChunMing Tang School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • XinYi Zheng School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China
  • WenYan Zhu School of Electronic and Information Engineering, Tianjin Polytechnic University, Tianjin 300387, China


Near infrared image, Colorization, Convolutional neural network, Image recognition.


Near-infrared(NIR) image colorization is the main research content in the field of current near-infrared image application. It has a wide range of application value. For the problem of image colorization, such as diffuse color and even color error, and can not be automated, A fast near-infrared image colorization model consisting of a lightweight image recognition network module and an image colorization CNN module with a fusion layer, firstly using a lightweight image recognition network for image recognition of near-infrared images, and then selecting from the IamgeNet image library The image of the same class as the scene is used as the training set of the colorized network. After training with the colored CNN module with the fusion layer, the near-infrared image is input as the testing set for colorization. The experimental results show that the color is colored by the algorithm. The image details are clear, the color transfer effect is good and the running speed is fast.


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How to Cite

Tang, C., Zheng, X., & Zhu, W. (2018). A Fast Near-Infrared Image Colorization Deep Learning Mode. American Scientific Research Journal for Engineering, Technology, and Sciences, 49(1), 118–130. Retrieved from https://www.asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/4533