A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single Image Dehazing✱
Authors: 
Chippy M Manu, Sreeni K GAuthors Info & Claims
Article No.: 1, Pages 1 - 9
Abstract
Dehazing is a difficult process because of the damage caused by the non-uniform fog and haze distribution in images. To address these issues, a Multi-Scale Residual dense Dehazing Network (MSRDNet) is proposed in this paper. A Contextual feature extraction module (CFM) for extracting multi-scale features and an Adaptive Residual Dense Module (ARDN) are used as sub-modules of MSRDNet. Moreover, all the hierarchical features extracted by each ARDN are fused, which helps to detect hazy maps of varying lengths with multi-scale features. This framework outperforms the state-of-the-art dehazing methods in removing haze while maintaining and restoring image detail in real-world and synthetic images captured under various scenarios.
Supplementary Material
References
[1]
Saeed Anwar and Nick Barnes. 2019. Densely Residual Laplacian Super-Resolution. arxiv:1906.12021 [eess.IV]
[2]
Haoran Bai, Jinshan Pan, Xinguang Xiang, and Jinhui Tang. 2022. Self-Guided Image Dehazing Using Progressive Feature Fusion. IEEE Transactions on Image Processing 31 (2022), 1217–1229. https://doi.org/10.1109/TIP.2022.3140609
[3]
D. Berman, T. Treibitz, and S. Avidan. 2016. Non-local Image Dehazing. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 1674–1682. https://doi.org/10.1109/CVPR.2016.185
[4]
Trung Minh Bui and Wonha Kim. 2018. Single Image Dehazing Using Color Ellipsoid Prior. IEEE Transactions on Image Processing 27, 2 (2018), 999–1009. https://doi.org/10.1109/TIP.2017.2771158
[5]
B. Cai, X. Xu, K. Jia, C. Qing, and D. Tao. 2016. DehazeNet: An End-to-End System for Single Image Haze Removal. IEEE Transactions on Image Processing 25, 11 (2016), 5187–5198. https://doi.org/10.1109/TIP.2016.2598681
[6]
Chen Chen, Minh N. Do, and Jue Wang. 2016. Robust Image and Video Dehazing with Visual Artifact Suppression via Gradient Residual Minimization. In Computer Vision – ECCV 2016, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). Springer International Publishing, Cham, 576–591.
[7]
Dongdong Chen, Mingming He, Qingnan Fan, Jing Liao, Liheng Zhang, Dongdong Hou, Lu Yuan, and Gang Hua. 2018. Gated Context Aggregation Network for Image Dehazing and Deraining. CoRR abs/1811.08747(2018). arxiv:1811.08747http://arxiv.org/abs/1811.08747
[8]
Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, and Sy-Yen Kuo. 2020. PMHLD: Patch Map-Based Hybrid Learning DehazeNet for Single Image Haze Removal. IEEE Transactions on Image Processing 29 (2020), 6773–6788. https://doi.org/10.1109/TIP.2020.2993407
[9]
Lark Kwon Choi, Jaehee You, and Alan Conrad Bovik. 2015. Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging. IEEE Transactions on Image Processing 24, 11 (2015), 3888–3901. https://doi.org/10.1109/TIP.2015.2456502
[10]
Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, and Ming-Hsuan Yang. 2020. Multi-Scale Boosted Dehazing Network with Dense Feature Fusion. CoRR abs/2004.13388(2020). arxiv:2004.13388https://arxiv.org/abs/2004.13388
[11]
Deniz Engin, Anil Genc, and Hazim Kemal Ekenel. 2018. Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing. In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 938–9388. https://doi.org/10.1109/CVPRW.2018.00127
[12]
Alona Golts, Daniel Freedman, and Michael Elad. 2020. Unsupervised Single Image Dehazing Using Dark Channel Prior Loss. IEEE Transactions on Image Processing 29 (2020), 2692–2701. https://doi.org/10.1109/TIP.2019.2952032
[13]
K. He, J. Sun, and X. Tang. 2011. Single Image Haze Removal Using Dark Channel Prior. IEEE Transactions on Pattern Analysis and Machine Intelligence 33, 12(2011), 2341–2353. https://doi.org/10.1109/TPAMI.2010.168
[14]
K. He, J. Sun, and X. Tang. 2013. Guided Image Filtering. IEEE Transactions on Pattern Analysis and Machine Intelligence 35, 6(2013), 1397–1409. https://doi.org/10.1109/TPAMI.2012.213
[15]
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2015. Deep Residual Learning for Image Recognition. arxiv:1512.03385 [cs.CV]
[16]
Ming Hong, Yuan Xie, Cuihua Li, and Yanyun Qu. 2020. Distilling Image Dehazing With Heterogeneous Task Imitation. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 3459–3468. https://doi.org/10.1109/CVPR42600.2020.00352
[17]
Po-Wen Hsieh and Pei-Chiang Shao. 2022. Variational contrast-saturation enhancement model for effective single image dehazing. Signal Processing 192(2022), 108396. https://doi.org/10.1016/j.sigpro.2021.108396
[18]
Gao Huang, Zhuang Liu, Laurens van der Maaten, and Kilian Q. Weinberger. 2018. Densely Connected Convolutional Networks. arxiv:1608.06993 [cs.CV]
[19]
Sergey Ioffe and Christian Szegedy. 2015. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. CoRR abs/1502.03167(2015). arXiv:1502.03167http://arxiv.org/abs/1502.03167
[20]
Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, and Alexei A. Efros. 2018. Image-to-Image Translation with Conditional Adversarial Networks. arxiv:1611.07004 [cs.CV]
[21]
Kui Jiang, Zhongyuan Wang, Peng Yi, Chen Chen, Baojin Huang, Yimin Luo, Jiayi Ma, and Junjun Jiang. 2020. Multi-Scale Progressive Fusion Network for Single Image Deraining. CoRR abs/2003.10985(2020). arxiv:2003.10985https://arxiv.org/abs/2003.10985
[22]
Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. 2019. DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better. CoRR abs/1908.03826(2019). arxiv:1908.03826http://arxiv.org/abs/1908.03826
[23]
A. Levin, D. Lischinski, and Y. Weiss. 2008. A Closed-Form Solution to Natural Image Matting. IEEE Transactions on Pattern Analysis and Machine Intelligence 30, 2(2008), 228–242. https://doi.org/10.1109/TPAMI.2007.1177
[24]
B. Li, X. Peng, Z. Wang, J. Xu, and D. Feng. 2017. AOD-Net: All-in-One Dehazing Network. In 2017 IEEE International Conference on Computer Vision (ICCV). 4780–4788. https://doi.org/10.1109/ICCV.2017.511
[25]
Zhengguo Li, Haiyan Shu, and Chaobing Zheng. 2021. Multi-Scale Single Image Dehazing Using Laplacian and Gaussian Pyramids. IEEE Transactions on Image Processing 30 (2021), 9270–9279. https://doi.org/10.1109/TIP.2021.3123551
[26]
Jing Liu, Haiyan Wu, Yuan Xie, Yanyun Qu, and Lizhuang Ma. 2020. Trident Dehazing Network. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1732–1741. https://doi.org/10.1109/CVPRW50498.2020.00223
[27]
Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. 2019. GridDehazeNet: Attention-Based Multi-Scale Network for Image Dehazing. CoRR abs/1908.03245(2019). arxiv:1908.03245http://arxiv.org/abs/1908.03245
[28]
Zheng Liu, Botao Xiao, Muhammad Alrabeiah, Keyan Wang, and Jun Chen. 2018. Generic Model-Agnostic Convolutional Neural Network for Single Image Dehazing. CoRR abs/1810.02862(2018). arXiv:1810.02862http://arxiv.org/abs/1810.02862
[29]
Chippy Manu and Sreeni K. G.2022. GANID: a novel generative adversarial network for image dehazing. The Visual Computer (06 2022), 1–14. https://doi.org/10.1007/s00371-022-02536-9
[30]
Chippy M Manu and Sreeni K G. 2021. MSDNet: A Novel Multi-Stage Progressive Image Dehazing Network. In Proceedings of the Twelfth Indian Conference on Computer Vision, Graphics and Image Processing(Jodhpur, India) (ICVGIP ’21). Association for Computing Machinery, New York, NY, USA, Article 4, 9 pages. https://doi.org/10.1145/3490035.3490261
[31]
Kangfu Mei, Aiwen Jiang, Juncheng Li, and Mingwen Wang. 2018. Progressive Feature Fusion Network for Realistic Image Dehazing. CoRR abs/1810.02283(2018). arXiv:1810.02283http://arxiv.org/abs/1810.02283
[32]
G. Meng, Y. Wang, J. Duan, S. Xiang, and C. Pan. 2013. Efficient Image Dehazing with Boundary Constraint and Contextual Regularization. In 2013 IEEE International Conference on Computer Vision. 617–624. https://doi.org/10.1109/ICCV.2013.82
[33]
Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. 2019. FFA-Net: Feature Fusion Attention Network for Single Image Dehazing. CoRR abs/1911.07559(2019). arxiv:1911.07559http://arxiv.org/abs/1911.07559
[34]
Y. Qu, Y. Chen, J. Huang, and Y. Xie. 2019. Enhanced Pix2pix Dehazing Network. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 8152–8160. https://doi.org/10.1109/CVPR.2019.00835
[35]
Dongdong Ren, Jinbao Li, Meng Han, and Minglei Shu. 2021. DNANet: Dense Nested Attention Network for Single Image Dehazing. In ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2035–2039. https://doi.org/10.1109/ICASSP39728.2021.9414179
[36]
Dongwei Ren, Wangmeng Zuo, Qinghua Hu, Pengfei Zhu, and Deyu Meng. 2019. Progressive Image Deraining Networks: A Better and Simpler Baseline. CoRR abs/1901.09221(2019). arxiv:1901.09221http://arxiv.org/abs/1901.09221
[37]
Wenqi Ren, Si Liu, Hua Zhang, Jinshan Pan, Xiaochun Cao, and Ming-Hsuan Yang. 2016. Single Image Dehazing via Multi-scale Convolutional Neural Networks. In Computer Vision – ECCV 2016, Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling (Eds.). Springer International Publishing, Cham, 154–169.
[38]
Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. 2018. Gated Fusion Network for Single Image Dehazing. (03 2018).
[39]
Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-Net: Convolutional Networks for Biomedical Image Segmentation. CoRR abs/1505.04597(2015). arxiv:1505.04597http://arxiv.org/abs/1505.04597
[40]
Sanchayan Santra, Ranjan Mondal, and Bhabatosh Chanda. 2018. Learning a Patch Quality Comparator for Single Image Dehazing. IEEE Transactions on Image Processing 27, 9 (2018), 4598–4607. https://doi.org/10.1109/TIP.2018.2841198
[41]
Shuaiyi Huang Ying Shen Shiyu Zhao, Lin Zhang and Shengjie Zhao. 2020. Dehazing Evaluation: Real-world Benchmark Datasets, Criteria and Baselines. IEEE Transactions on Image Processing(2020). https://doi.org/10.1109/TIP.2020.2995264
[42]
Ziyi Sun, Yunfeng Zhang, Fangxun Bao, Ping Wang, Xunxiang Yao, and Caiming Zhang. 2022. SADnet: Semi-Supervised Single Image Dehazing Method Based on an Attention Mechanism. ACM Trans. Multimedia Comput. Commun. Appl. 18, 2, Article 58 (feb 2022), 23 pages. https://doi.org/10.1145/3478457
[43]
R. Tan, N. Pettersson, and L. Petersson. 2007. Visibility Enhancement for Roads with Foggy or Hazy Scenes. 2007 IEEE Intelligent Vehicles Symposium(2007), 19–24.
[44]
Chunwei Tian, Lunke Fei, Wenxian Zheng, Yong Xu, Wangmeng Zuo, and Chia-Wen Lin. 2020. Deep Learning on Image Denoising: An overview. arxiv:1912.13171 [eess.IV]
[45]
Zhou Wang, A.C. Bovik, H.R. Sheikh, and E.P. Simoncelli. 2004. Image quality assessment: from error visibility to structural similarity. IEEE Transactions on Image Processing 13, 4 (2004), 600–612. https://doi.org/10.1109/TIP.2003.819861
[46]
Haiyan Wu, Jing Liu, Yuan Xie, Yanyun Qu, and Lizhuang Ma. 2020. Knowledge Transfer Dehazing Network for NonHomogeneous Dehazing. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 1975–1983. https://doi.org/10.1109/CVPRW50498.2020.00247
[47]
Lan Yan, Wenbo Zheng, Chao Gou, and Fei-Yue Wang. 2020. Feature Aggregation Attention Network for Single Image Dehazing. In 2020 IEEE International Conference on Image Processing (ICIP). 923–927. https://doi.org/10.1109/ICIP40778.2020.9191007
[48]
Dong Yang and Jian Sun. 2018. Proximal Dehaze-Net: A Prior Learning-Based Deep Network for Single Image Dehazing. In Proceedings of the European Conference on Computer Vision (ECCV).
[49]
F. Yu and V. Koltun. 2016. Multi-Scale Context Aggregation by Dilated Convolutions. CoRR abs/1511.07122(2016).
[50]
Jiahui Yu, Yuchen Fan, Jianchao Yang, Ning Xu, Zhaowen Wang, Xinchao Wang, and Thomas S. Huang. 2018. Wide Activation for Efficient and Accurate Image Super-Resolution. CoRR abs/1808.08718(2018). arXiv:1808.08718http://arxiv.org/abs/1808.08718
[51]
Hongguang Zhang, Yuchao Dai, Hongdong Li, and Piotr Koniusz. 2019. Deep Stacked Hierarchical Multi-patch Network for Image Deblurring. CoRR abs/1904.03468(2019). arxiv:1904.03468http://arxiv.org/abs/1904.03468
[52]
He Zhang and Vishal M. Patel. 2018. Densely Connected Pyramid Dehazing Network. arxiv:1803.08396 [cs.CV]
[53]
Jing Zhang, Yang Cao, Yang Wang, Zheng-Jun Zha, Chenglin Wen, and Chang Wen Chen. 2018. Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images. CoRR abs/1801.06302(2018). arXiv:1801.06302http://arxiv.org/abs/1801.06302
[54]
Xiaoqin Zhang, Tao Wang, Wenhan Luo, and Pengcheng Huang. 2021. Multi-Level Fusion and Attention-Guided CNN for Image Dehazing. IEEE Transactions on Circuits and Systems for Video Technology 31, 11(2021), 4162–4173. https://doi.org/10.1109/TCSVT.2020.3046625
[55]
Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, and Yun Fu. 2021. Residual Dense Network for Image Restoration. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 7(2021), 2480–2495. https://doi.org/10.1109/TPAMI.2020.2968521
[56]
Dong Zhao, Long Xu, Lin Ma, Jia Li, and Yihua Yan. 2021. Pyramid Global Context Network for Image Dehazing. IEEE Transactions on Circuits and Systems for Video Technology 31, 8(2021), 3037–3050. https://doi.org/10.1109/TCSVT.2020.3036992
[57]
Yupei Zheng, Xin Yu, Miaomiao Liu, and Shunli Zhang. 2019. Residual Multiscale Based Single Image Deraining. In BMVC.
[58]
Q. Zhu, J. Mai, and L. Shao. 2015. A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior. IEEE Transactions on Image Processing 24, 11 (2015), 3522–3533. https://doi.org/10.1109/TIP.2015.2446191
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- A Novel Multi-Scale Residual Dense Dehazing Network (MSRDNet) for Single Image Dehazing✱
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Published: 12 May 2023
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ICVGIP'22
ICVGIP'22: Thirteenth Indian Conference on Computer Vision, Graphics and Image Processing
December 8 - 10, 2022
Gandhinagar, India
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