The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
이 편지에서 우리는 심층 신경망과 준지도 학습 기반 디헤이징 알고리즘을 제안합니다. 디헤이징 네트워크는 피라미드 아키텍처를 사용하여 단일 흐릿한 이미지에서 안개가 없는 장면을 거친 순서에서 미세한 순서로 복구합니다. 서로 다른 스케일의 객체를 충실하게 복원하기 위해 계단식 다중 스케일 컨볼루셔널 블록을 피라미드의 각 레벨에 통합합니다. 네트워크의 기능 융합 및 전송은 인터리브된 잔여 연결로 구성된 경로를 사용하여 달성됩니다. 실제 환경의 복잡한 안개에 대한 더 나은 일반화를 위해 우리는 반 감독된 적대적 훈련을 가능하게 하는 판별기도 고안했습니다. 실험 결과는 제안된 작업이 더 높은 정량적 지표와 시각적으로 더 즐거운 출력을 통해 비교 작업보다 성능이 우수하다는 것을 보여줍니다. 또한 안개 속에서도 물체 감지의 견고성을 향상시킬 수 있습니다.
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Haoyu XU, Yuenan LI, "A Deep Neural Network for Coarse-to-Fine Image Dehazing with Interleaved Residual Connections and Semi-Supervised Training" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 5, pp. 1125-1129, May 2022, doi: 10.1587/transinf.2021EDL8052.
Abstract: In this letter, we propose a deep neural network and semi-supervised learning based dehazing algorithm. The dehazing network uses a pyramidal architecture to recover the haze-free scene from a single hazy image in a coarse-to-fine order. To faithfully restore the objects with different scales, we incorporate cascaded multi-scale convolutional blocks into each level of the pyramid. Feature fusion and transfer in the network are achieved using the paths constructed by interleaved residual connections. For better generalization to the complicated haze in real-world environments, we also devise a discriminator that enables semi-supervised adversarial training. Experimental results demonstrate that the proposed work outperforms comparative ones with higher quantitative metrics and more visually pleasant outputs. It can also enhance the robustness of object detection under haze.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2021EDL8052/_p
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@ARTICLE{e105-d_5_1125,
author={Haoyu XU, Yuenan LI, },
journal={IEICE TRANSACTIONS on Information},
title={A Deep Neural Network for Coarse-to-Fine Image Dehazing with Interleaved Residual Connections and Semi-Supervised Training},
year={2022},
volume={E105-D},
number={5},
pages={1125-1129},
abstract={In this letter, we propose a deep neural network and semi-supervised learning based dehazing algorithm. The dehazing network uses a pyramidal architecture to recover the haze-free scene from a single hazy image in a coarse-to-fine order. To faithfully restore the objects with different scales, we incorporate cascaded multi-scale convolutional blocks into each level of the pyramid. Feature fusion and transfer in the network are achieved using the paths constructed by interleaved residual connections. For better generalization to the complicated haze in real-world environments, we also devise a discriminator that enables semi-supervised adversarial training. Experimental results demonstrate that the proposed work outperforms comparative ones with higher quantitative metrics and more visually pleasant outputs. It can also enhance the robustness of object detection under haze.},
keywords={},
doi={10.1587/transinf.2021EDL8052},
ISSN={1745-1361},
month={May},}
부
TY - JOUR
TI - A Deep Neural Network for Coarse-to-Fine Image Dehazing with Interleaved Residual Connections and Semi-Supervised Training
T2 - IEICE TRANSACTIONS on Information
SP - 1125
EP - 1129
AU - Haoyu XU
AU - Yuenan LI
PY - 2022
DO - 10.1587/transinf.2021EDL8052
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2022
AB - In this letter, we propose a deep neural network and semi-supervised learning based dehazing algorithm. The dehazing network uses a pyramidal architecture to recover the haze-free scene from a single hazy image in a coarse-to-fine order. To faithfully restore the objects with different scales, we incorporate cascaded multi-scale convolutional blocks into each level of the pyramid. Feature fusion and transfer in the network are achieved using the paths constructed by interleaved residual connections. For better generalization to the complicated haze in real-world environments, we also devise a discriminator that enables semi-supervised adversarial training. Experimental results demonstrate that the proposed work outperforms comparative ones with higher quantitative metrics and more visually pleasant outputs. It can also enhance the robustness of object detection under haze.
ER -