웨이블릿 퓨전에 의한 딥러닝 색상화의 성능 향상
High-performance of Deep learning Colorization With Wavelet fusion
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초록
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.
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.
참고문헌 (8)
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- Choi Hyun, "Colorization Based on SegNet Architecture and Post-processing Using Wavelet Fusion", M.D., pp.2018
- Kamyar Nazeri, "Image Colorization with Generative Adversarial Networks", Faculty of Science, University of Ontario Institute of Technology, arXiv : 1803. 05400, ~ (2018)
- Vijay Badrinarayanan, "SegNet : A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation", Proceedings of IEEE Pattern Analysis and Machine Intelligence, 39(12), pp.2481~2495 (2017)
- Olaf Ronneberger, "U-Net : Convolutional Networks for Biomedical Image Segmentation", Proceedings of International Conference on Meidcal Image Computing and Computer-Assisted Intervention, pp.234~241 (2015)