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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.12
- 수록면
- 442 - 453 (12page)
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초록· 키워드
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Underwater color images suffer from low visibility and color cast effects caused by light attenuation by water and floating particles. This study applied single image enhancement techniques to enhance the quality of underwater images and compared their performance with real underwater images taken in Korean waters. Dark channel prior (DCP), gradient transform, image fusion, and generative adversarial networks (GAN), such as cycleGAN and underwater GAN (UGAN), were considered for single image enhancement. Their performance was evaluated in terms of underwater image quality measure, underwater color image quality evaluation, gray-world assumption, and blur metric. The DCP saturated the underwater images to a specific greenish or bluish color tone and reduced the brightness of the background signal. The gradient transform method with two transmission maps were sensitive to the light source and highlighted the region exposed to light. Although image fusion enabled reasonable color correction, the object details were lost due to the last fusion step. CycleGAN corrected overall color tone relatively well but generated artifacts in the background. UGAN showed good visual quality and obtained the highest scores against all figures of merit (FOMs) by compensating for the colors and visibility compared to the other single enhancement methods.
목차
ABSTRACT
1. Introduction
2. Single Image Enhancement
3. Results
4. Discussion
5. Conclusions
References
참고문헌 (54)
참고문헌 신청
Achantay, R., Hemamiz, S., Estraday, F., & Susstrunk, S. (2009). Frequency Tuned Salient Region Detection. Proceeding of IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA, 1597-1604. https://doi.org/10.1109/CVPR.2009.5206596
Ancuti, C.O., Ancuti, C., Vleeschouwer, C.D., & 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
Anwar, S., Li, C., & Porikli, F. (2018). Deep Underwater Image enhancement. arXiv:1807.03528. Retrieved from https://arxiv.org/abs/1807.03528
Anwar S. & Li, C. (2019). Diving Deeper into Underwater Image Enhancement: A Survey. arXiv:1907.07863. Retrieved 2019 from https://arxiv.org/abs/1907.07863
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv:1701.07875. Retrieved from https://arxiv.org/abs/1701.07875
Ancuti, C.O., Ancuti, C., Vleeschouwer, C.D., & 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
Anwar, S., Li, C., & Porikli, F. (2018). Deep Underwater Image enhancement. arXiv:1807.03528. Retrieved from https://arxiv.org/abs/1807.03528
Anwar S. & Li, C. (2019). Diving Deeper into Underwater Image Enhancement: A Survey. arXiv:1907.07863. Retrieved 2019 from https://arxiv.org/abs/1907.07863
Arjovsky, M., Chintala, S., & Bottou, L. (2017). Wasserstein GAN. arXiv:1701.07875. Retrieved from https://arxiv.org/abs/1701.07875
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