Abstract:
Near infrared (NIR) images are robust to ambient light and contain clear textures in low light condition. In this paper, we propose NIR image colorization using spatial a...Show MoreMetadata
Abstract:
Near infrared (NIR) images are robust to ambient light and contain clear textures in low light condition. In this paper, we propose NIR image colorization using spatial adaptive denormalization (SPADE) generator and grayscale approximated self-reconstruction. Compared with traditional image to image translation methods, the proposed NIR colorization pursues photorealism rather than generative diversity. The challenge of this task is NIR-RGB mis-registration in training data. We address this problem by separately extracting NIR texture and RGB color with an end to end SPADE based model. Moreover, the proposed method facilitates a more precise synthesis with a given low light RGB reference image. Experiments on an open NIR-RGB dataset verify that the proposed method effectively preserves NIR textures and RGB colors in the synthesized results and outperforms the baselines in terms of visual quality and quantitative assessments.
Published in: 2020 IEEE International Conference on Visual Communications and Image Processing (VCIP)
Date of Conference: 01-04 December 2020
Date Added to IEEE Xplore: 29 December 2020
ISBN Information: