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http://dx.doi.org/10.14372/IEMEK.2018.13.6.313

High-performance of Deep learning Colorization With Wavelet fusion  

Kim, Young-Back (Incheon National University)
Choi, Hyun (Incheon National University)
Cho, Joong-Hwee (Incheon National University)
Publication Information
IEMEK Journal of Embedded Systems and Applications / v.13, no.6, 2018 , pp. 313-319 More about this Journal
Abstract
We propose a post-processing algorithm to improve the quality of the RGB image generated by deep learning based colorization from the gray-scale image of an infrared camera. Wavelet fusion is used to generate a new luminance component of the RGB image luminance component from the deep learning model and the luminance component of the infrared camera. PSNR is increased for all experimental images by applying the proposed algorithm to RGB images generated by two deep learning models of SegNet and DCGAN. For the SegNet model, the average PSNR is improved by 1.3906dB at level 1 of the Haar wavelet method. For the DCGAN model, PSNR is improved 0.0759dB on the average at level 5 of the Daubechies wavelet method. It is also confirmed that the edge components are emphasized by the post-processing and the visibility is improved.
Keywords
Infrared; Colorization; Deep learning; Post-processing; Wavelet fusion;
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