电子学报 ›› 2023, Vol. 51 ›› Issue (1): 231-245.DOI: 10.12263/DZXB.20220838
收稿日期:
2022-07-17
修回日期:
2022-10-06
出版日期:
2023-01-25
通讯作者:
作者简介:
基金资助:
JIA Tong-yao1, ZHUO Li1,2(), LI Jia-feng1,2, ZHANG Jing1,2
Received:
2022-07-17
Revised:
2022-10-06
Online:
2023-01-25
Published:
2023-02-23
Corresponding author:
Supported by:
摘要:
户外视觉系统极易受到雾霾等恶劣天气影响,采集到的图像/视频质量严重下降,这不仅影响人眼的主观感受,也给后续的智能化分析带来严峻挑战.近年来,学者们将深度学习应用于图像去雾领域,取得了诸多的研究成果.但是雾霾图像场景复杂多变、降质因素众多,这对去雾算法的泛化能力提出了很高的要求.本文主要总结了近年来基于深度学习的单幅图像去雾技术研究进展.从先验知识和物理模型、映射关系建模、数据样本、知识迁移学习等角度出发,介绍了现有算法的研究思路、具体特点、优势与不足.尤其侧重于近两年来新出现的训练策略和网络结构,如元学习、小样本学习、域自适应、Transformer等.另外,本文在公共数据集上对比了各种代表性去雾算法的主客观性能、模型复杂度等,尤其是分析了去雾后的图像对于后续目标检测任务的影响,更全面地评价了现有算法性能的优劣,并探讨了未来可能的研究方向.
中图分类号:
贾童瑶, 卓力, 李嘉锋, 等. 基于深度学习的单幅图像去雾研究进展[J]. 电子学报, 2023, 51(1): 231-245.
Tong-yao JIA, Li ZHUO, Jia-feng LI, et al. Research Advances on Deep Learning Based Single Image Dehazing[J]. Acta Electronica Sinica, 2023, 51(1): 231-245.
数据集 | 时间 | 规模 | 生成方式 | 数据集链接 |
---|---|---|---|---|
HazeRD[ | 2017 | 75 | 模型合成 | https://labsites.rochester.edu/gsharma/research/computer-vision/hazerd/ |
I-HAZE[ | 2018 | 35 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire18/i-haze/ |
O-HAZE[ | 2018 | 45 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire18/o-haze/ |
Dense-HAZE[ | 2019 | 55 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire19/dense-haze/ |
NH-HAZE[ | 2020 | 55 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire20/nh-haze/ |
RESIDE[ | 2019 | 87 125 | 模型合成/真实 | https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0 |
MDID[ | 2020 | 30 346 | 模型合成 | — |
BeDDE[ | 2020 | 208 | 真实 | https://github.com/xiaofeng94/BeDDE-for-defogging |
表1 图像去雾常用数据集
数据集 | 时间 | 规模 | 生成方式 | 数据集链接 |
---|---|---|---|---|
HazeRD[ | 2017 | 75 | 模型合成 | https://labsites.rochester.edu/gsharma/research/computer-vision/hazerd/ |
I-HAZE[ | 2018 | 35 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire18/i-haze/ |
O-HAZE[ | 2018 | 45 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire18/o-haze/ |
Dense-HAZE[ | 2019 | 55 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire19/dense-haze/ |
NH-HAZE[ | 2020 | 55 | 真实 | https://data.vision.ee.ethz.ch/cvl/ntire20/nh-haze/ |
RESIDE[ | 2019 | 87 125 | 模型合成/真实 | https://sites.google.com/view/reside-dehaze-datasets/reside-standard?authuser=3D0 |
MDID[ | 2020 | 30 346 | 模型合成 | — |
BeDDE[ | 2020 | 208 | 真实 | https://github.com/xiaofeng94/BeDDE-for-defogging |
指标 | 全参考 | 无参考 | 应用领域 |
---|---|---|---|
PSNR | √ | 通用 | |
SSIM | √ | 通用 | |
BRISQUE[ | √ | 通用 | |
NIQE[ | √ | 通用 | |
MetaIQA[ | √ | 通用 | |
VI[ | √ | 去雾 | |
RI[ | √ | 去雾 | |
DHQI[ | √ | 去雾 |
表2 图像去雾常用评价指标
指标 | 全参考 | 无参考 | 应用领域 |
---|---|---|---|
PSNR | √ | 通用 | |
SSIM | √ | 通用 | |
BRISQUE[ | √ | 通用 | |
NIQE[ | √ | 通用 | |
MetaIQA[ | √ | 通用 | |
VI[ | √ | 去雾 | |
RI[ | √ | 去雾 | |
DHQI[ | √ | 去雾 |
数据集 | 算法 | DehazeNet[ | AOD-Net[ | EPDN[ | FFA-Net[ | CycleDehaze[ | RefineDNet[ | ZID[ | MADN[ | SSL[ | DADN[ |
---|---|---|---|---|---|---|---|---|---|---|---|
源码 链接 | https://github.com/caibolun/DehazeNet | https://github.com/weber0522bb/AODnet-by-pytorch | https://github.com/ErinChen1/EPDN | https://github.com/zhilin007/FFA-Net | https://github.com/niranjangavade98/CycleDehaze-Pytorch | https://github.com/xiaofeng94/RefineDNet-for-dehazing | https://github.com/liboyun/ZID | https://github.com/TongyJia/MADN | https://github.com/Prevalenter/semi-dehazing | https://github.com/HUSTSYJ/DA_dahazing | |
SOTS-I | PSNR | 21.14 | 19.06 | 21.55 | 36.39 | 17.81 | 24.23 | 19.83 | 26.69 | 21.87 | 25.82 |
SSIM | 0.847 2 | 0.850 4 | 0.907 1 | 0.988 6 | 0.809 5 | 0.943 1 | 0.835 3 | 0.932 3 | 0.874 3 | 0.928 8 | |
SOTS-O | PSNR | 24.75 | 22.71 | 22.32 | 33.57 | 19.95 | 20.61 | 19.83 | 28.13 | 25.36 | 26.72 |
SSIM | 0.927 0 | 0.911 2 | 0.868 3 | 0.984 0 | 0.885 8 | 0.879 8 | 0.835 3 | 0.957 5 | 0.921 0 | 0.922 0 | |
HazeRD | PSNR | 15.87 | 16.85 | 15.60 | 16.74 | 15.65 | 17.39 | 12.84 | 17.50 | 17.34 | 16.54 |
SSIM | 0.787 6 | 0.792 5 | 0.749 2 | 0.793 5 | 0.756 9 | 0.855 3 | 0.505 0 | 0.750 6 | 0.802 9 | 0.763 6 | |
HSTS | DHQI | 40.327 0 | 59.702 0 | 70.316 0 | 68.872 9 | — | 43.694 0 | 50.562 8 | 63.500 1 | 40.327 0 | 63.803 4 |
表3 图像去雾代表性算法的客观指标对比
数据集 | 算法 | DehazeNet[ | AOD-Net[ | EPDN[ | FFA-Net[ | CycleDehaze[ | RefineDNet[ | ZID[ | MADN[ | SSL[ | DADN[ |
---|---|---|---|---|---|---|---|---|---|---|---|
源码 链接 | https://github.com/caibolun/DehazeNet | https://github.com/weber0522bb/AODnet-by-pytorch | https://github.com/ErinChen1/EPDN | https://github.com/zhilin007/FFA-Net | https://github.com/niranjangavade98/CycleDehaze-Pytorch | https://github.com/xiaofeng94/RefineDNet-for-dehazing | https://github.com/liboyun/ZID | https://github.com/TongyJia/MADN | https://github.com/Prevalenter/semi-dehazing | https://github.com/HUSTSYJ/DA_dahazing | |
SOTS-I | PSNR | 21.14 | 19.06 | 21.55 | 36.39 | 17.81 | 24.23 | 19.83 | 26.69 | 21.87 | 25.82 |
SSIM | 0.847 2 | 0.850 4 | 0.907 1 | 0.988 6 | 0.809 5 | 0.943 1 | 0.835 3 | 0.932 3 | 0.874 3 | 0.928 8 | |
SOTS-O | PSNR | 24.75 | 22.71 | 22.32 | 33.57 | 19.95 | 20.61 | 19.83 | 28.13 | 25.36 | 26.72 |
SSIM | 0.927 0 | 0.911 2 | 0.868 3 | 0.984 0 | 0.885 8 | 0.879 8 | 0.835 3 | 0.957 5 | 0.921 0 | 0.922 0 | |
HazeRD | PSNR | 15.87 | 16.85 | 15.60 | 16.74 | 15.65 | 17.39 | 12.84 | 17.50 | 17.34 | 16.54 |
SSIM | 0.787 6 | 0.792 5 | 0.749 2 | 0.793 5 | 0.756 9 | 0.855 3 | 0.505 0 | 0.750 6 | 0.802 9 | 0.763 6 | |
HSTS | DHQI | 40.327 0 | 59.702 0 | 70.316 0 | 68.872 9 | — | 43.694 0 | 50.562 8 | 63.500 1 | 40.327 0 | 63.803 4 |
模型 | Person | Bicycle | Car | Motorbike | Bus | mAP |
---|---|---|---|---|---|---|
Hazy | 0.721 | 0.441 | 0.617 | 0.473 | 0.364 | 0.523 |
DehazeNet[ | 0.722 | 0.424 | 0.622 | 0.485 | 0.375 | 0.526 |
AOD-Net[ | 0.720 | 0.448 | 0.629 | 0.498 | 0.376 | 0.534 |
EPDN [ | 0.706 | 0.419 | 0.598 | 0.458 | 0.347 | 0.506 |
FFA-Net [ | 0.727 | 0.452 | 0.626 | 0.500 | 0.374 | 0.536 |
CycleDehaze [ | 0.684 | 0.421 | 0.581 | 0.424 | 0.325 | 0.487 |
RefineDNet [ | 0.726 | 0.459 | 0.625 | 0.499 | 0.231 | 0.536 |
ZID[ | 0.584 | 0.336 | 0.468 | 0.383 | 0.320 | 0.418 |
MADN[ | 0.726 | 0.454 | 0.628 | 0.499 | 0.374 | 0.536 |
SSL [ | 0.722 | 0.440 | 0.625 | 0.500 | 0.379 | 0.533 |
DADN [ | 0.716 | 0.431 | 0.653 | 0.495 | 0.397 | 0.538 |
表4 RTTS数据集上的目标检测结果
模型 | Person | Bicycle | Car | Motorbike | Bus | mAP |
---|---|---|---|---|---|---|
Hazy | 0.721 | 0.441 | 0.617 | 0.473 | 0.364 | 0.523 |
DehazeNet[ | 0.722 | 0.424 | 0.622 | 0.485 | 0.375 | 0.526 |
AOD-Net[ | 0.720 | 0.448 | 0.629 | 0.498 | 0.376 | 0.534 |
EPDN [ | 0.706 | 0.419 | 0.598 | 0.458 | 0.347 | 0.506 |
FFA-Net [ | 0.727 | 0.452 | 0.626 | 0.500 | 0.374 | 0.536 |
CycleDehaze [ | 0.684 | 0.421 | 0.581 | 0.424 | 0.325 | 0.487 |
RefineDNet [ | 0.726 | 0.459 | 0.625 | 0.499 | 0.231 | 0.536 |
ZID[ | 0.584 | 0.336 | 0.468 | 0.383 | 0.320 | 0.418 |
MADN[ | 0.726 | 0.454 | 0.628 | 0.499 | 0.374 | 0.536 |
SSL [ | 0.722 | 0.440 | 0.625 | 0.500 | 0.379 | 0.533 |
DADN [ | 0.716 | 0.431 | 0.653 | 0.495 | 0.397 | 0.538 |
模型 | 运行平台 | 模型参数量/M | 运行时间/s |
---|---|---|---|
DehazeNet[ | Matlab(C) | 0.008 | 1.339 9 |
AOD-Net[ | PyTorch(G) | 0.002 | 0.002 2 |
EPDN[ | PyTorch(G) | 17.379 | 0.025 4 |
FFA-Net[ | PyTorch(G) | 4.456 | 0.127 9 |
CycleDehaze[ | Tensorflow(G) | 11.383 | 99.000 0 |
RefineDNet[ | PyTorch(G) | 65.795 | 0.454 1 |
ZID[ | PyTorch(G) | 40.406 | 59.990 0 |
MADN[ | PyTorch(G) | 0.605 | 0.018 2 |
SSL[ | PyTorch(G) | 9.231 | 0.077 7 |
DADN[ | PyTorch(G) | 54.591 | 0.035 0 |
表5 算法模型参数量及运行速度对比
模型 | 运行平台 | 模型参数量/M | 运行时间/s |
---|---|---|---|
DehazeNet[ | Matlab(C) | 0.008 | 1.339 9 |
AOD-Net[ | PyTorch(G) | 0.002 | 0.002 2 |
EPDN[ | PyTorch(G) | 17.379 | 0.025 4 |
FFA-Net[ | PyTorch(G) | 4.456 | 0.127 9 |
CycleDehaze[ | Tensorflow(G) | 11.383 | 99.000 0 |
RefineDNet[ | PyTorch(G) | 65.795 | 0.454 1 |
ZID[ | PyTorch(G) | 40.406 | 59.990 0 |
MADN[ | PyTorch(G) | 0.605 | 0.018 2 |
SSL[ | PyTorch(G) | 9.231 | 0.077 7 |
DADN[ | PyTorch(G) | 54.591 | 0.035 0 |
1 | LI J F, ZHUO L, ZHANG H, et al. Effective data-driven technology for efficient vision-based outdoor industrial systems[J]. IEEE Transactions on Industrial Informatics, 2020, 16(7): 4344-4354. |
2 | 郭璠, 蔡自兴, 谢斌, 等. 图像去雾技术研究综述与展望[J]. 计算机应用, 2010, 30(9): 2417-2421. |
GUO F, CAI Z X, XIE B, et al. Review and prospect of image dehazing techniques[J]. Journal of Computer Applications, 2010, 30(9): 2417-2421. (in Chinese) | |
3 | 沈琛, 曹风云, 杨雪洁. 夜间图像去雾研究综述与展望[J]. 电子测量与仪器学报, 2020, 34(11): 101-114. |
SHEN C, CAO F Y, YANG X J. Review and prospect of nighttime haze removal[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(11): 101-114. (in Chinese) | |
4 | 郑凤仙, 王夏黎, 何丹丹, 等. 单幅图像去雾算法研究综述[J]. 计算机工程与应用, 2022, 58(3): 1-14. |
ZHENG F X, WANG X L, HE D D, et al. Survey of single image defogging algorithm[J]. Computer Engineering and Applications, 2022, 58(3): 1-14. (in Chinese) | |
5 | HE K M, SUN J, TANG X O. Single image haze removal using dark channel prior[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(12): 2341-2353. |
6 | ZHU Q S, MAI J M, SHAO L. A fast single image haze removal algorithm using color attenuation prior[J]. IEEE Transactions on Image Processing, 2015, 24(11): 3522-3533. |
7 | BERMAN D, TREIBITZ T, AVIDAN S. Non-local image dehazing[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1674-1682. |
8 | MCCARTNEY E J, HALL F F. Optics of the atmosphere: Scattering by molecules and particles[J]. Physics Today, 1977, 30(5): 76-77. |
9 | NAYAR S K, NARASIMHAN S G. Vision in bad weather[C]//Proceedings of the Seventh IEEE International Conference on Computer Vision. Kerkyra: IEEE, 1999: 820-827. |
10 | NARASIMHAN S G, NAYAR S K. Removing weather effects from monochrome images[C]//Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Kauai: IEEE, 2001: II. |
11 | JU M Y, DING C, REN W Q, et al. IDE: Image dehazing and exposure using an enhanced atmospheric scattering model[J]. IEEE Transactions on Image Processing, 2021, 30: 2180-2192. |
12 | JU M Y, DING C, GUO C A, et al. IDRLP: Image dehazing using region line prior[J]. IEEE Transactions on Image Processing, 2021, 30: 9043-9057. |
13 | CHEN W T, DING J J, KUO S Y. PMS-Net: Robust haze removal based on patch map for single images[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 11673-11681. |
14 | CAI B L, XU X M, JIA K, et al. DehazeNet: An end-to-end system for single image haze removal[J]. IEEE Transactions on Image Processing, 2016, 25(11): 5187-5198. |
15 | LI B Y, PENG X L, WANG Z Y, et al. AOD-Net: All-in-one dehazing network[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4780-4788. |
16 | ZHANG J, TAO D C. FAMED-net: A fast and accurate multi-scale end-to-end dehazing network[J]. IEEE Transactions on Image Processing, 2020, 29: 72-84. |
17 | ZHANG H, PATEL V M. Densely connected pyramid dehazing network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3194-3203. |
18 | LI Y N, MIAO Q G, OUYANG W L, et al. LAP-Net: Level-aware progressive network for image dehazing[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 3275-3284. |
19 | LIU Y, PAN J S, REN J, et al. Learning deep priors for image dehazing[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 2492-2500. |
20 | LI R D, PAN J S, HE M, et al. Task-oriented network for image dehazing[J]. IEEE Transactions on Image Processing, 2020, 29: 6523-6534. |
21 | BAI H R, PAN J S, XIANG X G, et al. Self-guided image dehazing using progressive feature fusion[J]. IEEE Transactions on Image Processing, 2022, 31: 1217-1229. |
22 | BADRINARAYANAN V, KENDALL A, CIPOLLA R. SegNet: A deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(12): 2481-2495. |
23 | GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11): 139-144. |
24 | RADFORD A, METZ L, CHINTALA S, et al. Unsupervised representation learning with deep convolutional generative adversarial networks[C]//The 4th International Conference on Learning Representations. San Juan: International Conference on Learning Representations, 2016: 149803. |
25 | MIRZA M, OSINDERO S. Conditional generative adversarial nets. (2014-11-06)[2022-07]. . |
26 | ARJOVSKY M, CHINTALA S, BOTTOU L. Wasserstein GAN. (2017-01-26)[2022-07]. . |
27 | LI R D, PAN J S, LI Z C, et al. Single image dehazing via conditional generative adversarial network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 8202-8211. |
28 | REN W Q, MA L, ZHANG J W, et al. Gated fusion network for single image dehazing[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3253-3261. |
29 | QU Y Y, CHEN Y Z, HUANG J Y, et al. Enhanced Pix2pix dehazing network[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 8152-8160. |
30 | LIU X, SUGANUMA M, SUN Z, et al. Dual residual networks leveraging the potential of paired operations for image restoration[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 7000-7009. |
31 | LIU X H, MA Y R, SHI Z H, et al. GridDehazeNet: attention-based multi-scale network for image dehazing[C]//2019 IEEE/CVF International Conference on Computer Vision. Seoul: IEEE, 2019: 7313-7322. |
32 | DONG H, PAN J S, XIANG L, et al. Multi-scale boosted dehazing network with dense feature fusion[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 2154-2164. |
33 | QIN X, WANG Z L, BAI Y C, et al. FFA-net: Feature fusion attention network for single image dehazing[C]//The Thirty-Fourth AAAI Conference on Artificial Intelligence. New York: AAAI Press, 2020, 34(7): 11908-11915. |
34 | WU H Y, QU Y Y, LIN S H, et al. Contrastive learning for compact single image dehazing[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 10546-10555. |
35 | ZHENG Z R, REN W Q, CAO X C, et al. Ultra-high-definition image dehazing via multi-guided bilateral learning[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 16180-16189. |
36 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//31st Annual Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017: 6000-6010. |
37 | DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[EB/OL]. (2020-10-22)[2022-07]. . |
38 | CHEN H T, WANG Y H, GUO T Y, et al. Pre-trained image processing transformer[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 12294-12305. |
39 | TOLSTIKHIN I, HOULSBY N, KOLESNIKOV A, et al. MLP-mixer: An all-MLP architecture for vision[EB/OL]. (2021-05-04)[2022-07]. . |
40 | LIU H, DAI Z, SO D R, et al. Pay attention to MLPs[C]//Advances in Neural Information Processing Systems. Virtual Conference: NeurIPS, 2021, 34: 9204-9215. |
4141] TOUVRON H, BOJANOWSKI P, CARON M, et al. ResMLP: Feedforward networks for image classification with data-efficient training[EB/OL]. (2021-05-07)[2022-07]. . | |
42 | TU Z Z, TALEBI H, ZHANG H, et al. MAXIM: multi-axis MLP for image processing[C]//2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New Orleans: IEEE, 2022: 5759-5770. |
43 | ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2242-2251. |
44 | YI Z L, ZHANG H, TAN P, et al. DualGAN: Unsupervised dual learning for image-to-image translation[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2868-2876. |
45 | KIM T, CHA M, KIM H, et al. Learning to discover cross-domain relations with generative adversarial networks[C]//Proceedings of the 34th International Conference on Machine Learning. Sydney: JMLR.org, 2017: 1857-1865. |
46 | ENGIN D, GENC A, EKENEL H K. Cycle-dehaze: Enhanced CycleGAN for single image dehazing[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 938-9388. |
47 | LI Y N, LIU Y H, YAN Q X, et al. Deep dehazing network with latent ensembling architecture and adversarial learning[J]. IEEE Transactions on Image Processing, 2021, 30: 1354-1368. |
48 | LI J F, LI Y P, ZHUO L, et al. USID-Net: Unsupervised single image dehazing network via disentangled representations[J/OL]. IEEE Transactions on Multimedia. DOI: 10.1109/TMM.2022.3163554 . |
49 | ZHAO S Y, ZHANG L, SHEN Y, et al. RefineDNet: A weakly supervised refinement framework for single image dehazing[J]. IEEE Transactions on Image Processing, 2021, 30: 3391-3404. |
50 | LI B Y, GOU Y B, LIU J Z, et al. Zero-shot image dehazing[J]. IEEE Transactions on Image Processing, 2020, 29: 8457-8466. |
51 | KAR A, DHARA S K, SEN D, et al. Zero-shot single image restoration through controlled perturbation of koschmieder's model[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 16200-16210. |
52 | HINTON G, VINYALS O, DEAN J. Distilling the knowledge in a neural network[EB/OL]. (2015-03-09)[2022-07]. . |
53 | HONG M, XIE Y, LI C H, et al. Distilling image dehazing with heterogeneous task imitation[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 3459-3468. |
54 | JIA T Y, LI J F, ZHUO L, et al. Effective meta-attention dehazing networks for vision-based outdoor industrial systems[J]. IEEE Transactions on Industrial Informatics, 2022, 18(3): 1511-1520. |
55 | LI L, DONG Y L, REN W Q, et al. Semi-supervised image dehazing[J]. IEEE Transactions on Image Processing, 2020, 29: 2766-2779. |
56 | SHAO Y J, LI L, REN W Q, et al. Domain adaptation for image dehazing[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 2805-2814. |
57 | ZHANG K S, LI Y N. Single image dehazing via semi-supervised domain translation and architecture search[J]. IEEE Signal Processing Letters, 2021, 28: 2127-2131. |
58 | CHEN Z Y, WANG Y C, YANG Y, et al. PSD: principled synthetic-to-real dehazing guided by physical priors[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Nashville: IEEE, 2021: 7176-7185. |
59 | ZHANG Y F, DING L, SHARMA G. HazeRD: An outdoor scene dataset and benchmark for single image dehazing[C]//2017 IEEE International Conference on Image Processing. Beijing: IEEE, 2017: 3205-3209. |
60 | ANCUTI C, ANCUTI C O, TIMOFTE R, et al. I-HAZE: A dehazing benchmark with real hazy and haze-free indoor images[C]//International Conference on Advanced Concepts for Intelligent Vision Systems.Poitiers: Springer, 2018: 620-631. |
61 | ANCUTI C O, ANCUTI C, TIMOFTE R, et al. O-HAZE: A dehazing benchmark with real hazy and haze-free outdoor images[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City: IEEE, 2018: 867-8678. |
62 | ANCUTI C O, ANCUTI C, SBERT M, et al. Dense-haze: A benchmark for image dehazing with dense-haze and haze-free images[C]//2019 IEEE International Conference on Image Processing. Taipei: IEEE, 2019: 1014-1018. |
63 | ANCUTI C O, ANCUTI C, TIMOFTE R. NH-HAZE: An image dehazing benchmark with non-homogeneous hazy and haze-free images[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Seattle: IEEE, 2020: 1798-1805. |
64 | LI B Y, REN W Q, FU D P, et al. Benchmarking single-image dehazing and beyond[J]. IEEE Transactions on Image Processing, 2019, 28(1): 492-505. |
65 | WANG K, ZHUO L, LI J F, et al. Learning an enhancement convolutional neural network for multi-degraded images[J].Sensing and Imaging, 2020, 21(1): 1-15. |
66 | ZHAO S Y, ZHANG L, HUANG S Y, et al. Dehazing evaluation: Real-world benchmark datasets, criteria, and baselines[J]. IEEE Transactions on Image Processing, 2020, 29: 6947-6962. |
67 | SILBERMAN N, HOIEM D, KOHLI P, et al. Indoor segmentation and support inference from RGBD images[C]//European Conference on Computer Vision. Florence: Springer, 2012: 746-760. |
68 | 韩翰, 卓力, 张菁, 等. 基于深度学习的无参考图像质量评价综述[J]. 测控技术, 2022, 41(4): 1-10. |
HAN H, ZHUO L, ZHANG J, et al. Summary of no-reference image quality assessment based on deep learning[J]. Measurement & Control Technology, 2022, 41(4): 1-10. (in Chinese) | |
69 | MITTAL A, MOORTHY A K, BOVIK A C. No-reference image quality assessment in the spatial domain[J]. IEEE Transactions on Image Processing, 2012, 21(12): 4695-4708. |
70 | MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209-212. |
71 | ZHU H C, LI L D, WU J J, et al. MetaIQA: Deep meta-learning for no-reference image quality assessment[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Seattle: IEEE, 2020: 14131-14140. |
72 | MIN X K, ZHAI G T, GU K, et al. Objective quality evaluation of dehazed images[J]. IEEE Transactions on Intelligent Transportation Systems, 2019, 20(8): 2879-2892. |
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