[1]王德文,陈威,苏攀.基于粗到细的多尺度单幅图像去雾方法[J].智能系统学报,2024,19(5):1102-1110.[doi:10.11992/tis.202305005]
WANG Dewen,CHEN Wei,SU Pan.Single-image dehazing via a coarse-to-fine multiscale approach[J].CAAI Transactions on Intelligent Systems,2024,19(5):1102-1110.[doi:10.11992/tis.202305005]
点击复制
WANG Dewen,CHEN Wei,SU Pan.Single-image dehazing via a coarse-to-fine multiscale approach[J].CAAI Transactions on Intelligent Systems,2024,19(5):1102-1110.[doi:10.11992/tis.202305005]
基于粗到细的多尺度单幅图像去雾方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
19
期数:
2024年第5期
页码:
1102-1110
栏目:
学术论文—机器学习
出版日期:
2024-09-05
- Title:
- Single-image dehazing via a coarse-to-fine multiscale approach
- Keywords:
- image dehazing; coarse to fine; multiscale feature fusion; residual feature attention; asymmetric feature fusion; adaptive mixup; contrastive regularization; positive and negative sample
- 分类号:
- TP391
- 文献标志码:
- 2024-08-28
- 摘要:
- 为了解决现有图像去雾算法易出现细节纹理丢失、颜色失真或对非均匀浓雾处理不彻底的问题，提出一种基于粗到细的多尺度单幅图像去雾方法。首先，主干网络使用残差特征注意力模块对有雾图像进行特征提取；其次，将不同尺度的输入图像进行卷积预处理，通过多尺度特征融合模块将预处理的浅层特征与主干网络融合；再次，将不同粒度的非对称特征进行有效融合；最后，将浅层信息与深层信息自适应混合输出，通过对比正则损失构建正负样本信息，使得去雾图像更接近无雾图像。实验结果表明,与已有代表性的去雾方法相比，提出的方法能对合成数据集与真实数据集进行有效去雾，在细节保留与色彩还原上优于对比方法。
- Abstract:
- A single-image dehazing method based on a coarse-to-fine, multiscale approach is proposed to address the shortcomings of existing dehazing methods, which often result in loss of detailed texture, color distortion, or incomplete processing of non-uniform dense fog. First, features are extracted from foggy images using the residual feature attention module of the backbone network. Second, images at different scales are preprocessed using convolution, and the preprocessed shallow features are fused with the backbone network through a multiscale feature fusion module. Asymmetric features at different levels are then effectively fused. Finally, shallow information is adaptively combined with deep information at the output. Compared with contrastive regularization loss, positive and negative sample information is constructed to make the defogged image more similar to a fog-free image. The experimental results show that, compared with existing representative defogging methods, the proposed method effectively defogs synthetic and real datasets and outperforms comparison methods in terms of detail retention and color reproduction.
- 参考文献/References:
-
[1] 王道累, 张天宇. 图像去雾算法的综述及分析[J]. 图学学报, 2020, 41(6): 861-870.
WANG Daolei, ZHANG Tianyu. Review and analysis of image defogging algorithm[J]. Journal of graphics, 2020, 41(6): 861-870.
[2] NARASIMHAN S G, NAYAR S K. Vision and the atmosphere[J]. International journal of computer vision, 2002, 48(3): 233-254.
[3] HE Kaiming, SUN Jian, TANG Xiaoou. Single image haze removal using dark channel prior[J]. IEEE transactions on pattern analysis and machine intelligence, 2011, 33(12): 2341-2353.
[4] 张世辉, 路佳琪, 宋丹丹, 等. 基于多尺度特征结合细节恢复的单幅图像去雾方法[J]. 电子与信息学报, 2022, 44(11): 3967-3976.
ZHANG Shihui, LU Jiaqi, SONG Dandan, et al. Single image dehazing method based on multi-scale features combined with detail recovery[J]. Journal of electronics & information technology, 2022, 44(11): 3967-3976.
[5] ZHU Qingsong, MAI Jiaming, SHAO Ling. A fast single image haze removal algorithm using color attenuation prior[J]. IEEE transactions on image processing, 2015, 24(11): 3522-3533.
[6] 王科平, 杨艺, 费树岷. 雾霾图像清晰化算法综述[J]. 智能系统学报, 2023, 18(2): 217-230.
WANG Keping, YANG Yi, FEI Shumin. Review of hazy image sharpening methods[J]. CAAI transactions on intelligent systems, 2023, 18(2): 217-230.
[7] CAI Bolun, XU Xiangmin, JIA Kui, et al. DehazeNet: an end-to-end system for single image haze removal[J]. IEEE transactions on image processing, 2016, 25(11): 5187-5198.
[8] LI Boyi, PENG Xiulian, WANG Zhangyang, et al. AOD-net: all-in-one dehazing network[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 4780-4788.
[9] REN Wenqi, LIU Si, ZHANG Hua, et al. Single image dehazing via multi-scale convolutional neural networks[C]//European Conference on Computer Vision. Cham: Springer, 2016: 154-169.
[10] CHEN Dongdong, HE Mingming, FAN Qingnan, et al. Gated context aggregation network for image dehazing and deraining[C]//2019 IEEE Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2019: 1375-1383.
[11] QU Yanyun, CHEN Yizi, HUANG Jingying, et al. Enhanced pix2pix dehazing network[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 8152-8160.
[12] QIN Xu, WANG Zhilin, BAI Yuanchao, et al. FFA-net: feature fusion attention network for single image dehazing[J]. Proceedings of the AAAI conference on artificial intelligence, 2020, 34(7): 11908-11915.
[13] WU Haiyan, QU Yanyun, LIN Shaohui, 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.
[14] 高峰, 汲胜昌, 郭洁, 等. 采用对比学习的多阶段Transformer图像去雾方法[J]. 西安交通大学学报, 2023, 57(1): 195-210.
GAO Feng, JI Shengchang, GUO Jie, et al. A multi-stage transformer network for image dehazing based on contrastive learning[J]. Journal of Xi’an Jiaotong University, 2023, 57(1): 195-210.
[15] ZHANG Hongguang, DAI Yuchao, LI Hongdong, et al. Deep stacked hierarchical multi-patch network for image deblurring[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 5971-5979.
[16] RONNEBERGER O, FISCHER P, BROX T. U-Net: convolutional networks for biomedical image segmentation[M]//Lecture Notes in Computer Science. Cham: Springer, 2015: 234-241.
[17] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778.
[18] 范新南, 赵忠鑫, 严炜, 等. 结合注意力机制的多尺度特征融合图像去雾算法[J]. 计算机科学, 2022, 49(5): 50-57.
FAN Xinnan, ZHAO Zhongxin, YAN Wei, et al. Multi-scale feature fusion image dehazing algorithm combined with attention mechanism[J]. Computer science, 2022, 49(5): 50-57.
[19] CHO S J, JI S W, HONG J P, et al. Rethinking coarse-to-fine approach in single image deblurring[C]//2021 IEEE/CVF International Conference on Computer Vision. Montreal: IEEE, 2021: 4621-4630.
[20] ZHANG Hongyi, CISSE M, DAUPHIN Y N, et al. Mixup: beyond empirical risk minimization[EB/OL]. (2017-10-25)[2023-05-05]. https://arxiv.org/abs/1710.09412.
[21] 张重生, 陈杰, 李岐龙, 等. 深度对比学习综述[J]. 自动化学报, 2023, 49(1): 15-39.
ZHANG Chongsheng, CHEN Jie, LI Qilong, et al. Deep contrastive learning: a survey[J]. Acta automatica sinica, 2023, 49(1): 15-39.
[22] LEDIG C, THEIS L, HUSZáR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 105-114.
[23] LI Boyi, REN Wenqi, FU Dengpan, et al. Benchmarking single-image dehazing and beyond[J]. IEEE transactions on image processing, 2019, 28(1): 492-505.
[24] 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.
[25] ANCUTI C O, ANCUTI C, VASLUIANU F A, et al. NTIRE 2021 NonHomogeneous dehazing challenge report[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Nashville: IEEE, 2021: 627-646.
[26] HE Tong, ZHANG Zhi, ZHANG Hang, et al. Bag of tricks for image classification with convolutional neural networks[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Long Beach: IEEE, 2019: 558-567.
[27] DONG Hang, PAN Jinshan, XIANG Lei, 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.
- 相似文献/References:
-
[1]陈珂,柯文德,许波,等.改进的彩色图像去雾效果评价方法[J].智能系统学报,2015,10(5):803.[doi:10.11992/tis.201406003]
CHEN Ke,KE Wende,XU Bo,et al.An improved assessment method for the color image defogging effect[J].CAAI Transactions on Intelligent Systems,2015,10():803.[doi:10.11992/tis.201406003]
[2]王科平,杨艺,费树岷.雾霾图像清晰化算法综述[J].智能系统学报,2023,18(2):217.[doi:10.11992/tis.202110029]
WANG Keping,YANG Yi,FEI Shumin.Review of hazy image sharpening methods[J].CAAI Transactions on Intelligent Systems,2023,18():217.[doi:10.11992/tis.202110029]
[3]郑卓然,魏绎汶,贾修一.基于全局与局部感知网络的超高清图像去雾方法[J].智能系统学报,2024,19(1):89.[doi:10.11992/tis.202304013]
ZHENG Zhuoran,WEI Yiwen,JIA Xiuyi.UHD image dehazing method based on global and local aware networks[J].CAAI Transactions on Intelligent Systems,2024,19():89.[doi:10.11992/tis.202304013]
备注/Memo
收稿日期:2023-5-5。
基金项目:河北省自然科学基金项目(F2021502013)；河北省高等学校科学技术研究项目(QN2023181)；中央高校基本科研业务费专项资金项目(2021MS094).
作者简介:王德文，副教授，主要研究方向为人工智能、图像处理。主持或参与国家自然科学基金项目 4 项，获河北省科技进步奖 3 项，以第一完成人获得国家专利授权 3 项，发表学术论文 50 余篇。E-mail：wdewen@gmail.com;陈威，硕士研究生，主要研究方向为人工智能、图像处理。E-mail：644691154@qq.com;苏攀，副教授，博士，主要研究方向为机器学习、模糊系统、图像处理。E-mail：supan@ncepu.edu.cn。
通讯作者:王德文. E-mail：wdewen@gmail.com
更新日期/Last Update:
2024-09-05