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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.2
- 수록면
- 32 - 40 (9page)
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초록· 키워드
오류제보하기
Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.
목차
ABSTRACT
1. Introduction
2. GAN-based Underwater Optical Image Improvement Techniques
3. Image Fusion-Based Underwater Optical Image Improvement Techniques
4. Results
5. Conclusions
References
참고문헌 (23)
참고문헌 신청
Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., & Bekaert, P. (2017). Color Balance and Fusion for Underwater Image Enhancement. IEEE Transactions on Image Processing, 27(1), 379–393. https://doi.org/10.1109/TIP.2017.2759252
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein Generative Adversarial Networks. Proceedings of the 34th International Conference on Machine Learning, PMLR, 70, 214–223.
Bharal, S. (2015). Review of Underwater Image Enhancement Techniques. International Research Journal of Engineering and Technology, 2(3), 340–344.
Chen, X., Yu, J., Kong, S., Wu, Z., Fang, X., & Wen, L. (2019). Towards Real-Time Advancement of Underwater Visual Quality with GAN. In IEEE Transactions on Industrial Electronics, 66(12), 9350–9359. https://doi.org/10.1109/TIE.2019.2893840
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A Large-Scale Hierarchical Image Database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255. https://doi.org/10.1109/CVPR.2009.5206848
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein Generative Adversarial Networks. Proceedings of the 34th International Conference on Machine Learning, PMLR, 70, 214–223.
Bharal, S. (2015). Review of Underwater Image Enhancement Techniques. International Research Journal of Engineering and Technology, 2(3), 340–344.
Chen, X., Yu, J., Kong, S., Wu, Z., Fang, X., & Wen, L. (2019). Towards Real-Time Advancement of Underwater Visual Quality with GAN. In IEEE Transactions on Industrial Electronics, 66(12), 9350–9359. https://doi.org/10.1109/TIE.2019.2893840
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., & Fei-Fei, L. (2009, June). Imagenet: A Large-Scale Hierarchical Image Database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–255. https://doi.org/10.1109/CVPR.2009.5206848
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