A study on selective dehazing system based on haze detection network

Hong-Il Seo; Ju-Won Bae; Hyung-Rae Cho; Ju-Hyeon Seong; Dong-Hoan Seo

doi:10.5916/jamet.2020.44.6.494

추천

검색

질문

자료유형: 학술저널

저자정보: Hong-Il Seo (Korea Maritime & Ocean University) Ju-Won Bae (Korea Maritime & Ocean University) Hyung-Rae Cho (Korea Maritime & Ocean University) Ju-Hyeon Seong (Korea Maritime & Ocean University) Dong-Hoan Seo (Korea Maritime & and Ocean University)

저널정보: 한국마린엔지니어링학회 Journal of Advanced Marine Engineering and Technology (JAMET) 한국마린엔지니어링학회지 제44권 제6호

발행연도: 2020.12

수록면: 494 - 499 (6page)

DOI: 10.5916/jamet.2020.44.6.494

이용수

📌

연구주제

📖

연구배경

🔬

연구방법

🏆

연구결과

초록· 키워드

오류제보하기

Image dehazing, which aims to recover a clear image solely from a hazy or foggy image, is a particularly challenging task. Many studies have recently been conducted to improve the performance of image dehazing using deep neural networks. However, existing approaches do not consider changes in haze density, and thus even if a clear image is input, distortions such as sharpening may occur. In addition, because the number of datasets available in deep learning, whose contents are image pairs of hazy and corresponding haze-free (ground truth) indoor images, is quite limited, the haze removal performance may be reduced. To solve this problem, in this paper, a selective dehazing system is proposed that combines a haze detection network and a dehazing network. The proposed haze detection network is designed using a CNN structure to determine the haze density of the input image, and the use of the dehazing network is determined. The proposed dehazing network uses the U-Net model to efficiently learn only a limited number of datasets. To evaluate the performance of the proposed network, only 45 O-Hazes were used. The result of haze detection shows that the probability of detecting a haze image is more than 99% and the probability of detecting a haze-free image is 97.9%. Dehazing evaluation results improved the PSNR and SSIM by more than 10% compared to existing networks.

#Image processing #Deep neural network #Dehazing #Haze detection

참고문헌 (20)

참고문헌 신청

C. Ancuti / 2018 / NTIRE 2018 challenge on image dehazing : Methods and results / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops :891~901

C. O. Ancuti / 2019 / NTIRE 2019 image dehazing challenge report / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops :0~0

C. O. Ancuti / 2020 / NTIRE 2020 challenge on nonhomogeneous dehazing / Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops :490~491

D. Engin / 2018 / Cycle-dehaze : Enhanced CycleGAN for single image dehazing / Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops :825~833

E. J. McCartney / 1976 / Optics of the Atmosphere: Scattering by Molecules and Particles / John Wiley and Sons

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!