Image-to-Image Translation Network for Aerial Imagery Defogging
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초록
Drones are extensively being used in many facets of life. Despite tremendous technological advancements, certain atmospheric turbidities such as rain, haze, and fog pose serious challenges to good performance of drones. Focusing on the idea of removing fog from a given image, we proposed a novel singleimage-based image defogging network, that do not require any additional, physical, or prior information regarding the given foggy image. In this study, we used Generative Adversarial Network (GAN)-based technique to remove fog from an input foggy image. The proposed method outperforms the state-of-the-art in terms of PSNR, SSIM, Contrast Measurement, and Mean Visual Score (MVS). The extensive experiments performed on both synthetic and real-world testing images validate the effectiveness of the proposed technique.
Abstract
Drones are extensively being used in many facets of life. Despite tremendous technological advancements, certain atmospheric turbidities such as rain, haze, and fog pose serious challenges to good performance of drones. Focusing on the idea of removing fog from a given image, we proposed a novel singleimage-based image defogging network, that do not require any additional, physical, or prior information regarding the given foggy image. In this study, we used Generative Adversarial Network (GAN)-based technique to remove fog from an input foggy image. The proposed method outperforms the state-of-the-art in terms of PSNR, SSIM, Contrast Measurement, and Mean Visual Score (MVS). The extensive experiments performed on both synthetic and real-world testing images validate the effectiveness of the proposed technique.
참고문헌 (28)
- P. Garg, "Isdnet: Ai-enabled instance segmentation of aerial scenes for smart cities", ACM Trans. Internet Technol, 1(1), pp.1~19 (2020)
- G. -S. Xia, "DOTA: A large-scale dataset for object detection in aerial images", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.3974~3983 (2018)
- E. Vattapparamban, "Drones for smart cities: Issues in cybersecurity, privacy, and public safety", 2016 international wireless communications and mobile computing conference (IWCMC), pp.216~221 (2016), IEEE
- C. Kyrkou, "Drones : Augmenting our quality of life", IEEE Potentials, 38(1), pp.30~36 (2019)
- 노먼 칸, "Efficient Sport Videos Classification via Convolutional Neural Network", 한국차세대컴퓨팅학회 논문지, 16(6), pp.7~16 (2020), 한국차세대컴퓨팅학회[DOI]
- 히크마트 야르, "Fire Detection via Effective Vision Transformers", 한국차세대컴퓨팅학회 논문지, 17(5), pp.21~30 (2021), 한국차세대컴퓨팅학회[DOI]
- S. U. Khan, "Deep-ReID: deep features and autoencoder assisted image patching strategy for person re-identification in smart cities surveillance", Multimedia Tools and Applications, 1(22), pp.1~22 (2021)
- S. G. Narasimhan, "Interactive (de) weathering of an image using physical models", IEEE Workshop on color and photometric Methods in computer Vision, 6(6.4), pp.1~ (2003)
- G. Meng, "Efficient image dehazing with boundary constraint and contextual regularization", Proceedings of the IEEE international conference on computer vision, pp.617~624 (2013)
- Y. Y. Schechner, "Instant dehazing of images using polarization", Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 1, pp.1~1 (2001), IEEE
- S. G. Narasimhan, "Chromatic framework for vision in bad weather", Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000(Cat. No. PR00662), 1, pp.598~605 (2000), IEEE
- R. Fattal, "Dehazing using color-lines", ACM transactions on graphics(TOG), 34(1), pp.1~14 (2014)
- R. T. Tan, "Visibility in bad weather from a single image", 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1~8 (2008), IEEE
- K. He, "Single image haze removal using dark channel prior", IEEE transactions on pattern analysis and machine intelligence, 33(12), pp.2341~2353 (2010)
- Y. Shao, "Domain adaptation for image dehazing", Proceedings of the IEEE, 2020, 2808~2817
- H. Yar, "Lung Nodule Detection and Classification using 2D and 3D Convolution Neural Networks (CNNs)", Artificial Intelligence and Internet of Things, pp.365~386
- N. Khan, "SD-Net : Understanding overcrowded scenes in real-time via an efficient dilated convolutional neural network", Journal of Real-Time Image Processing, pp.1~15 (2020)
- S. U. Khan, "Cover the violence : A novel Deep Learning-Based approach towards violence detection in movies", Applied Sciences, 9(22), pp.4963~ (2019)
- B. Li, "Aod-net : All-in-one dehazing network", Proceedings of the IEEE international conference on computer vision, pp.4770~4778 (2017)
- Y. Qu, "Enhanced pix2pix dehazing network", Proceedings of the IEEE, 2019, 8160~8168
- R. Li, "Single image dehazing via conditional generative adversarial network", Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.8202~8211 (2018)
- I. Goodfellow, "Generative adversarial nets", Advances in neural information processing systems, 27 (2014)
- P. Isola, "Image-to-image translation with conditional adversarial networks", Proceedings of the IEEE conference on computer vision and pattern recognition, pp.1125~1134 (2017)
- O. Ronneberger, "U-net:Convolutional networks for biomedical image segmentation", International Conference on Medical image computing and computer-assisted intervention, pp.234~241 (2015), Springer
- H. Zhao, "Loss functions for image restoration with neural networks", IEEE Transactions on computational imaging, 3(1), pp.47~57 (2016)
- A. Hore, "Image quality metrics:PSNR vs. SSIM", 2010 20th international conference on pattern recognition, pp.2366~2369 (2010), IEEE
- 심재영, "Single image dehazing based on contrast enhancement", International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2011), IEEE