research-article

Open access

Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution

Authors:

Robby T. TanAuthors Info & Claims

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

Pages 2446 - 2457

https://doi.org/10.1145/3581783.3611884

Published: 27 October 2023 Publication History

Abstract

Visibility in hazy nighttime scenes is frequently reduced by multiple factors, including low light, intense glow, light scattering, and the presence of multicolored light sources. Existing nighttime dehazing methods often struggle with handling glow or low-light conditions, resulting in either excessively dark visuals or unsuppressed glow outputs. In this paper, we enhance the visibility from a single nighttime haze image by suppressing glow and enhancing low-light regions. To handle glow effects, our framework learns from the rendered glow pairs. Specifically, a light source aware network is proposed to detect light sources of night images, followed by the APSF (Angular Point Spread Function)-guided glow rendering. Our framework is then trained on the rendered images, resulting in glow suppression. Moreover, we utilize gradient-adaptive convolution, to capture edges and textures in hazy scenes. By leveraging extracted edges and textures, we enhance the contrast of the scene without losing important structural details. To boost low-light intensity, our network learns an attention map, then adjusted by gamma correction. This attention has high values on low-light regions and low values on haze and glow regions. Extensive evaluation on real nighttime haze images, demonstrates the effectiveness of our method. Our experiments demonstrate that our method achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13% on GTA5 nighttime haze dataset. Our data and code is available at: https://github.com/jinyeying/nighttime_dehaze.

References

[1]

Cosmin Ancuti, Codruta O Ancuti, Christophe De Vleeschouwer, and Alan C Bovik. 2016. Night-time dehazing by fusion. In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2256--2260.

[2]

Cosmin Ancuti, Codruta O Ancuti, Christophe De Vleeschouwer, and Alan C Bovik. 2020. Day and night-time dehazing by local airlight estimation. IEEE Transactions on Image Processing, Vol. 29 (2020), 6264--6275.

[3]

Sriparna Banerjee and Sheli Sinha Chaudhuri. 2021. Nighttime image-dehazing: a review and quantitative benchmarking. Archives of Computational Methods in Engineering, Vol. 28, 4 (2021), 2943--2975.

[4]

Dana Berman, Shai Avidan, et al. 2016. Non-local image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1674--1682.

[5]

Dana Berman, Tali Treibitz, and Shai Avidan. 2018. Single image dehazing using haze-lines. IEEE transactions on pattern analysis and machine intelligence, Vol. 42, 3 (2018), 720--734.

[6]

Bolun Cai, Xiangmin Xu, Kui Jia, Chunmei Qing, and Dacheng Tao. 2016. Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, Vol. 25, 11 (2016), 5187--5198.

Digital Library

[7]

Subrahmanyan Chandrasekhar. 2013. Radiative transfer. Courier Corporation.

[8]

Sixiang Chen, Tian Ye, Yun Liu, Erkang Chen, Jun Shi, and Jingchun Zhou. 2022. Snowformer: Scale-aware transformer via context interaction for single image desnowing. arXiv preprint arXiv:2208.09703 (2022).

[9]

Sixiang Chen, Tian Ye, Yun Liu, Taodong Liao, Jingxia Jiang, Erkang Chen, and Peng Chen. 2023. MSP-former: Multi-scale projection transformer for single image desnowing. In ICASSP 2023--2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 1--5.

[10]

Zeyuan Chen, Yangchao Wang, Yang Yang, and Dong Liu. 2021. PSD: Principled synthetic-to-real dehazing guided by physical priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 7180--7189.

[11]

Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, and Chen Change Loy. 2022. Flare7k: A phenomenological nighttime flare removal dataset. Advances in Neural Information Processing Systems, Vol. 35 (2022), 3926--3937.

[12]

Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Yihang Luo, and Chen Change Loy. 2023 a. Flare7K: Mixing Synthetic and Real Datasets for Nighttime Flare Removal and Beyond. arXiv preprint arXiv:2306.04236 (2023).

[13]

Yuekun Dai, Chongyi Li, Shangchen Zhou, Ruicheng Feng, Qingpeng Zhu, Qianhui Sun, Wenxiu Sun, Chen Change Loy, Jinwei Gu, Shuai Liu, et al. 2023 b. MIPI 2023 Challenge on Nighttime Flare Removal: Methods and Results. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2852--2862.

[14]

Yuekun Dai, Yihang Luo, Shangchen Zhou, Chongyi Li, and Chen Change Loy. 2023 c. Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 20783--20791.

[15]

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. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2157--2167.

[16]

Raanan Fattal. 2008. Single image dehazing. ACM transactions on graphics (TOG), Vol. 27, 3 (2008), 1--9.

[17]

Alona Golts, Daniel Freedman, and Michael Elad. 2019. Unsupervised single image dehazing using dark channel prior loss. IEEE Transactions on Image Processing, Vol. 29 (2019), 2692--2701.

[18]

Chunle Guo, Ruiqi Wu, Xin Jin, Linghao Han, Zhi Chai, Weidong Zhang, and Chongyi Li. 2023. Underwater Ranker: Learn Which Is Better and How to Be Better. In Proceedings of the AAAI conference on artificial intelligence.

Digital Library

[19]

Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, and Chongyi Li. 2022b. Image Dehazing Transformer with Transmission-Aware 3D Position Embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5812--5820.

[20]

Lanqing Guo, Renjie Wan, Wenhan Yang, Alex Kot, and Bihan Wen. 2022a. Enhancing low-light images in real world via cross-image disentanglement. arXiv preprint arXiv:2201.03145 (2022).

[21]

Xiaojie Guo, Yu Li, and Haibin Ling. 2016. LIME: Low-light image enhancement via illumination map estimation. IEEE Transactions on image processing, Vol. 26, 2 (2016), 982--993.

Digital Library

[22]

Kaiming He, Jian Sun, and Xiaoou Tang. 2010. Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, Vol. 33, 12 (2010), 2341--2353.

[23]

Kaiming He, Jian Sun, and Xiaoou Tang. 2011. Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, Vol. 33, 12 (2011), 2341--2353.

[24]

Lu-Yao Huang, Jia-Li Yin, Bo-Hao Chen, and Shao-Zhen Ye. 2019. Towards Unsupervised Single Image Dehazing With Deep Learning. In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 2741--2745.

[25]

Akira Ishimaru et al. 1978. Wave propagation and scattering in random media. Vol. 2. Academic press New York.

[26]

Xin Jin, Jia-Wen Xiao, Ling-Hao Han, Chunle Guo, Ruixun Zhang, Xialei Liu, and Chongyi Li. 2023 b. Lighting Every Darkness in Two Pairs: A Calibration-Free Pipeline for RAW Denoising. Proceedings of the IEEE/CVF International Conference on Computer Vision.

[27]

Yeying Jin, Ruoteng Li, Wenhan Yang, and Robby T Tan. 2023 a. Estimating reflectance layer from a single image: Integrating reflectance guidance and shadow/specular aware learning. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37. 1069--1077.

Digital Library

[28]

Yeying Jin, Aashish Sharma, and Robby T Tan. 2021. DC-ShadowNet: Single-Image Hard and Soft Shadow Removal Using Unsupervised Domain-Classifier Guided Network. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5027--5036.

[29]

Yeying Jin, Wending Yan, Wenhan Yang, and Robby T Tan. 2022a. Structure Representation Network and Uncertainty Feedback Learning for Dense Non-Uniform Fog Removal. In Proceedings of the Asian Conference on Computer Vision. 2041--2058.

[30]

Yeying Jin, Wenhan Yang, and Robby T Tan. 2022b. Unsupervised night image enhancement: When layer decomposition meets light-effects suppression. In European Conference on Computer Vision. Springer, 404--421.

Digital Library

[31]

Yeying Jin, Wenhan Yang, Wei Ye, Yuan Yuan, and Robby T Tan. 2022c. ShadowDiffusion: Diffusion-based Shadow Removal using Classifier-driven Attention and Structure Preservation. arXiv preprint arXiv:2211.08089 (2022).

[32]

Harald Koschmieder. 1924. Theorie der horizontalen Sichtweite. Beitrage zur Physik der freien Atmosphare (1924), 33--53.

[33]

Shiba Kuanar, Dwarikanath Mahapatra, Monalisa Bilas, and KR Rao. 2022. Multi-path dilated convolution network for haze and glow removal in nighttime images. The Visual Computer, Vol. 38, 3 (2022), 1121--1134.

Digital Library

[34]

Anat Levin, Dani Lischinski, and Yair Weiss. 2007. A closed-form solution to natural image matting. IEEE transactions on pattern analysis and machine intelligence, Vol. 30, 2 (2007), 228--242.

[35]

Boyun Li, Yuanbiao Gou, Shuhang Gu, Jerry Zitao Liu, Joey Tianyi Zhou, and Xi Peng. 2021. You only look yourself: Unsupervised and untrained single image dehazing neural network. International Journal of Computer Vision, Vol. 129, 5 (2021), 1754--1767.

[36]

Boyun Li, Yuanbiao Gou, Jerry Zitao Liu, Hongyuan Zhu, Joey Tianyi Zhou, and Xi Peng. 2020a. Zero-shot image dehazing. IEEE Transactions on Image Processing, Vol. 29 (2020), 8457--8466.

[37]

Boyi Li, Xiulian Peng, Zhangyang Wang, Jizheng Xu, and Dan Feng. 2017. Aod-net: All-in-one dehazing network. In Proceedings of the IEEE international conference on computer vision. 4770--4778.

[38]

Lerenhan Li, Yunlong Dong, Wenqi Ren, Jinshan Pan, Changxin Gao, Nong Sang, and Ming-Hsuan Yang. 2019. Semi-supervised image dehazing. IEEE Transactions on Image Processing, Vol. 29 (2019), 2766--2779.

[39]

Runde Li, Jinshan Pan, Zechao Li, and Jinhui Tang. 2018. Single image dehazing via conditional generative adversarial network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8202--8211.

[40]

Xin Li, Xin Jin, Jianxin Lin, Sen Liu, Yaojun Wu, Tao Yu, Wei Zhou, and Zhibo Chen. 2020b. Learning disentangled feature representation for hybrid-distorted image restoration. In Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23-28, 2020, Proceedings, Part XXIX 16. Springer, 313--329.

Digital Library

[41]

Xin Li, Bingchen Li, Xin Jin, Cuiling Lan, and Zhibo Chen. 2023. Learning Distortion Invariant Representation for Image Restoration from A Causality Perspective. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1714--1724.

[42]

Yu Li and Michael S Brown. 2014. Single image layer separation using relative smoothness. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2752--2759.

Digital Library

[43]

Yi Li, Yi Chang, Yan Gao, Changfeng Yu, and Luxin Yan. 2022. Physically Disentangled Intra-and Inter-Domain Adaptation for Varicolored Haze Removal. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 5841--5850.

[44]

Yu Li, Fangfang Guo, Robby T Tan, and Michael S Brown. 2014. A contrast enhancement framework with JPEG artifacts suppression. In European conference on computer vision. Springer, 174--188.

[45]

Yu Li, Robby T Tan, and Michael S Brown. 2015a. Nighttime haze removal with glow and multiple light colors. In Proceedings of the IEEE international conference on computer vision. 226--234.

Digital Library

[46]

Zhuwen Li, Ping Tan, Robby T Tan, Danping Zou, Steven Zhiying Zhou, and Loong-Fah Cheong. 2015b. Simultaneous video defogging and stereo reconstruction. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4988--4997.

[47]

Wei Liu, Xianxu Hou, Jiang Duan, and Guoping Qiu. 2020. End-to-end single image fog removal using enhanced cycle consistent adversarial networks. IEEE Transactions on Image Processing, Vol. 29 (2020), 7819--7833.

Digital Library

[48]

Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. 2019. Griddehazenet: Attention-based multi-scale network for image dehazing. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 7314--7323.

[49]

Yun Liu, Zhongsheng Yan, Sixiang Chen, Tian Ye, Wenqi Ren, and Erkang Chen. 2023 a. NightHazeFormer: Single Nighttime Haze Removal Using Prior Query Transformer. arXiv preprint arXiv:2305.09533 (2023).

[50]

Yun Liu, Zhongsheng Yan, Jinge Tan, and Yuche Li. 2023 b. Multi-Purpose Oriented Single Nighttime Image Haze Removal Based on Unified Variational Retinex Model. IEEE Transactions on Circuits and Systems for Video Technology, Vol. 33, 4 (2023), 1643--1657. https://doi.org/10.1109/TCSVT.2022.3214430

Digital Library

[51]

Yun Liu, Zhongsheng Yan, Aimin Wu, Tian Ye, and Yuche Li. 2022a. Nighttime image dehazing based on variational decomposition model. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition Workshop. 640--649.

[52]

Yun Liu, Zhongsheng Yan, Tian Ye, Aimin Wu, and Yuche Li. 2022b. Single nighttime image dehazing based on unified variational decomposition model and multi-scale contrast enhancement. Engineering Applications of Artificial Intelligence, Vol. 116 (2022), 105373.

Digital Library

[53]

William Edgar Knowles Middleton. 1957. Vision through the atmosphere. Springer.

[54]

Srinivasa G Narasimhan and Shree K Nayar. 2000. Chromatic framework for vision in bad weather. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662), Vol. 1. IEEE, 598--605.

[55]

Srinivasa G Narasimhan and Shree K Nayar. 2003. Shedding light on the weather. In 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings., Vol. 1. IEEE, I--I.

[56]

Dubok Park, David K Han, and Hanseok Ko. 2016. Nighttime image dehazing with local atmospheric light and weighted entropy. In 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2261--2265.

[57]

Soo-Chang Pei and Tzu-Yen Lee. 2012. Nighttime haze removal using color transfer pre-processing and dark channel prior. In 2012 19th IEEE International conference on image processing. IEEE, 957--960.

[58]

Matti Pietikäinen. 2010. Local binary patterns. Scholarpedia, Vol. 5, 3 (2010), 9775.

[59]

Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. 2020. FFA-Net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34. 11908--11915.

[60]

Yanyun Qu, Yizi Chen, Jingying Huang, and Yuan Xie. 2019. Enhanced Pix2pix Dehazing Network. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 8160--8168.

[61]

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 European conference on computer vision. Springer, 154--169.

[62]

Wenqi Ren, Lin Ma, Jiawei Zhang, Jinshan Pan, Xiaochun Cao, Wei Liu, and Ming-Hsuan Yang. 2018. Gated fusion network for single image dehazing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3253--3261.

[63]

Yuanjie Shao, Lerenhan Li, Wenqi Ren, Changxin Gao, and Nong Sang. 2020. Domain Adaptation for Image Dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2808--2817.

[64]

Aashish Sharma, Loong-Fah Cheong, Lionel Heng, and Robby T Tan. 2020a. Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions. In 2020 International Conference on 3D Vision (3DV). IEEE, 23--31.

[65]

Aashish Sharma and Robby T Tan. 2021. Nighttime Visibility Enhancement by Increasing the Dynamic Range and Suppression of Light Effects. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11977--11986.

[66]

Aashish Sharma, Robby T Tan, and Loong-Fah Cheong. 2020b. Single-Image Camera Response Function Using Prediction Consistency and Gradual Refinement. In Proceedings of the Asian Conference on Computer Vision.

[67]

Yuda Song, Zhuqing He, Hui Qian, and Xin Du. 2023. Vision transformers for single image dehazing. IEEE Transactions on Image Processing, Vol. 32 (2023), 1927--1941.

Digital Library

[68]

Hang Su, Varun Jampani, Deqing Sun, Orazio Gallo, Erik Learned-Miller, and Jan Kautz. 2019. Pixel-adaptive convolutional neural networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 11166--11175.

[69]

Zhuo Su, Wenzhe Liu, Zitong Yu, Dewen Hu, Qing Liao, Qi Tian, Matti Pietik?inen, and Li Liu. 2021. Pixel difference networks for efficient edge detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 5117--5127.

[70]

Robby T Tan. 2008. Visibility in bad weather from a single image. In 2008 IEEE conference on computer vision and pattern recognition. IEEE, 1--8.

[71]

Qunfang Tang, Jie Yang, Xiangjian He, Wenjing Jia, Qingnian Zhang, and Haibo Liu. 2021. Nighttime image dehazing based on Retinex and dark channel prior using Taylor series expansion. Computer Vision and Image Understanding, Vol. 202 (2021), 103086.

[72]

Carlo Tomasi and Roberto Manduchi. 1998. Bilateral filtering for gray and color images. In Sixth international conference on computer vision (IEEE Cat. No. 98CH36271). IEEE, 839--846.

[73]

Jiadong Wang, Xinyuan Qian, Malu Zhang, Robby T Tan, and Haizhou Li. 2023 a. Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 14653--14662.

[74]

Wenhui Wang, Anna Wang, and Chen Liu. 2022b. Variational Single Nighttime Image Haze Removal With a Gray Haze-Line Prior. IEEE Transactions on Image Processing, Vol. 31 (2022), 1349--1363.

[75]

Yufei Wang, Renjie Wan, Wenhan Yang, Haoliang Li, Lap-Pui Chau, and Alex Kot. 2022a. Low-light image enhancement with normalizing flow. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36. 2604--2612.

[76]

Yufei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C Kot, and Bihan Wen. 2023 b. ExposureDiffusion: Learning to Expose for Low-light Image Enhancement. arXiv preprint arXiv:2307.07710 (2023).

[77]

Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. 2021. Contrastive learning for compact single image dehazing. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 10551--10560.

[78]

Ruiqi Wu, Zhengpeng Duan, Chunle Guo, Zhi Chai, and Chongyi Li. 2023 a. RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[79]

Wanyu Wu, Wei Wang, Zheng Wang, Kui Jiang, and Xin Xu. 2023 b. From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement. arxiv: 2307.16783 [cs.CV]

[80]

Boxue Xiao, Zhuoran Zheng, Yunliang Zhuang, Chen Lyu, and Xiuyi Jia. 2022. Single UHD image dehazing via interpretable pyramid network. Available at SSRN 4134196 (2022).

[81]

Zeyu Xiao, Jiawang Bai, Zhihe Lu, and Zhiwei Xiong. 2023. A dive into sam prior in image restoration. arXiv preprint arXiv:2305.13620 (2023).

[82]

Zeyu Xiao, Xueyang Fu, Jie Huang, Zhen Cheng, and Zhiwei Xiong. 2021. Space-time distillation for video super-resolution. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2113--2122.

[83]

Zeyu Xiao, Zhiwei Xiong, Xueyang Fu, Dong Liu, and Zheng-Jun Zha. 2020. Space-time video super-resolution using temporal profiles. In Proceedings of the 28th ACM International Conference on Multimedia. 664--672.

Digital Library

[84]

Wending Yan, Robby T Tan, and Dengxin Dai. 2020. Nighttime defogging using high-low frequency decomposition and grayscale-color networks. In European Conference on Computer Vision. Springer, 473--488.

Digital Library

[85]

Minmin Yang, Jianchang Liu, and Zhengguo Li. 2018. Superpixel-based single nighttime image haze removal. IEEE transactions on multimedia, Vol. 20, 11 (2018), 3008--3018.

Digital Library

[86]

Yang Yang, Chaoyue Wang, Risheng Liu, Lin Zhang, Xiaojie Guo, and Dacheng Tao. 2022. Self-Augmented Unpaired Image Dehazing via Density and Depth Decomposition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2037--2046.

[87]

Tian Ye, Sixiang Chen, Yun Liu, Yi Ye, Jinbin Bai, and Erkang Chen. 2022a. Towards real-time high-definition image snow removal: Efficient pyramid network with asymmetrical encoder-decoder architecture. In Proceedings of the Asian Conference on Computer Vision. 366--381.

[88]

Tian Ye, Sixiang Chen, Yun Liu, Yi Ye, Erkang Chen, and Yuche Li. 2022b. Underwater light field retention: Neural rendering for underwater imaging. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 488--497.

[89]

Tian Ye, Yunchen Zhang, Mingchao Jiang, Liang Chen, Yun Liu, Sixiang Chen, and Erkang Chen. 2022c. Perceiving and modeling density for image dehazing. In European Conference on Computer Vision. Springer, 130--145.

Digital Library

[90]

Hu Yu, Naishan Zheng, Man Zhou, Jie Huang, Zeyu Xiao, and Feng Zhao. 2022. Frequency and spatial dual guidance for image dehazing. In Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23-27, 2022, Proceedings, Part XIX. Springer, 181--198.

[91]

Teng Yu, Kang Song, Pu Miao, Guowei Yang, Huan Yang, and Chenglizhao Chen. 2019. Nighttime single image dehazing via pixel-wise alpha blending. IEEE Access, Vol. 7 (2019), 114619--114630.

[92]

He Zhang and Vishal M Patel. 2018. Densely connected pyramid dehazing network. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3194--3203.

[93]

Jing Zhang, Yang Cao, Shuai Fang, Yu Kang, and Chang Wen Chen. 2017. Fast haze removal for nighttime image using maximum reflectance prior. In Proceedings of the IEEE conference on computer vision and pattern recognition. 7418--7426.

[94]

Jing Zhang, Yang Cao, and Zengfu Wang. 2014. Nighttime haze removal based on a new imaging model. IEEE, 4557--4561.

[95]

Jing Zhang, Yang Cao, Zheng-Jun Zha, and Dacheng Tao. 2020. Nighttime dehazing with a synthetic benchmark. In Proceedings of the 28th ACM International Conference on Multimedia. 2355--2363.

Digital Library

[96]

Shiyu Zhao, Lin Zhang, Ying Shen, and Yicong Zhou. 2021. RefineDNet: A weakly supervised refinement framework for single image dehazing. IEEE Transactions on Image Processing, Vol. 30 (2021), 3391--3404.

Digital Library

[97]

Zhuoran Zheng, Wenqi Ren, Xiaochun Cao, Xiaobin Hu, Tao Wang, Fenglong Song, and Xiuyi Jia. 2021. Ultra-high-definition image dehazing via multi-guided bilateral learning. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 16180--16189.

[98]

Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. 2017. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. In Computer Vision (ICCV), 2017 IEEE International Conference on.

Cited By

Cui YWang QLi CRen WKnoll A(2025)EENet: An effective and efficient network for single image dehazingPattern Recognition10.1016/j.patcog.2024.111074158(111074)Online publication date: Feb-2025
https://doi.org/10.1016/j.patcog.2024.111074
D. Thepade SShah KRajput SA.A PC.M NC.D TM.V S(2024)Weighted Fusion of Pre-processing Techniques for Neural Network-based Image Haze RemovalInternational Research Journal of Multidisciplinary Technovation10.54392/irjmt2421(1-11)Online publication date: 6-Feb-2024
https://doi.org/10.54392/irjmt2421
Zhou YWei HLiang JMa FYang RRen LLi X(2024)Robust polarimetric dehazing algorithm based on low-rank approximation and multiple virtual-exposure fusionPhotonics Research10.1364/PRJ.52237012:8(1640)Online publication date: 26-Jul-2024
https://doi.org/10.1364/PRJ.522370
Show More Cited By

Index Terms

Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks

Recommendations

A Visibility-Guided Fusion Framework for Fast Nighttime Image Dehazing
Pattern Recognition and Computer Vision
Abstract
Defogging is an important image enhancement and restoration technique that is widely used for various computer vision and computational photography applications. While the vast majority of currently available defogging methods work well for ...
Multi-path dilated convolution network for haze and glow removal in nighttime images
Abstract
In this paper, we address the single-image haze removal problem in nighttime scenes. The night haze removal is a severely ill-posed problem due to the presence of various visible night light sources with varying colors and non-uniform ...
Nighttime Haze Removal with Glow Decomposition Using GAN
Pattern Recognition
Abstract
In this paper, we investigate the problem of a single image haze removal in the nighttime. Glow effect is inherently existing in nighttime scenes due to multiple light sources with various colors and prominent glows. As the glow obscures the color ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

MM '23: Proceedings of the 31st ACM International Conference on Multimedia

October 2023

9913 pages

ISBN:9798400701085

DOI:10.1145/3581783

General Chairs:
Abdulmotaleb El Saddik
University of Ottawa, Canada & MBZUAI, UAE
,
Tao Mei
HiDream.ai, China
,
Rita Cucchiara
University of Modena and Reggio Emilia, Italy
,
Program Chairs:
Marco Bertini
University of Florence, Italy
,
Diana Patricia Tobon Vallejo
Unversidad de Medellin, Colombia
,
Pradeep K. Atrey
University at Albany, State University of New York, USA
,
M. Shamim Hossain
M. Shamim Hossain (King Saud University, KSA

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 October 2023

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

AISG

Conference

MM '23

Sponsor:

SIGMM

MM '23: The 31st ACM International Conference on Multimedia

October 29 - November 3, 2023

Ottawa ON, Canada

Acceptance Rates

Overall Acceptance Rate 995 of 4,171 submissions, 24%

Upcoming Conference

MM '24

Sponsor:
sigmm

The 32nd ACM International Conference on Multimedia

October 28 - November 1, 2024

Melbourne , VIC , Australia

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

15
Total Citations
View Citations
830
Total Downloads

Downloads (Last 12 months)830
Downloads (Last 6 weeks)99

Reflects downloads up to 15 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Cui YWang QLi CRen WKnoll A(2025)EENet: An effective and efficient network for single image dehazingPattern Recognition10.1016/j.patcog.2024.111074158(111074)Online publication date: Feb-2025
https://doi.org/10.1016/j.patcog.2024.111074
D. Thepade SShah KRajput SA.A PC.M NC.D TM.V S(2024)Weighted Fusion of Pre-processing Techniques for Neural Network-based Image Haze RemovalInternational Research Journal of Multidisciplinary Technovation10.54392/irjmt2421(1-11)Online publication date: 6-Feb-2024
https://doi.org/10.54392/irjmt2421
Zhou YWei HLiang JMa FYang RRen LLi X(2024)Robust polarimetric dehazing algorithm based on low-rank approximation and multiple virtual-exposure fusionPhotonics Research10.1364/PRJ.52237012:8(1640)Online publication date: 26-Jul-2024
https://doi.org/10.1364/PRJ.522370
Wang XChen XRen WHan ZFan HTang YLiu L(2024)Compensation Atmospheric Scattering Model and Two-Branch Network for Single Image DehazingIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33868388:4(2880-2896)Online publication date: Aug-2024
https://doi.org/10.1109/TETCI.2024.3386838
Zhu JLiu FXue ZLuo WPeng HHe JFu ZLuo D(2024)Cable Tunnel Waterlogging Detection for Low-Light and Interference Based on Three-Stage RecognitionIEEE Access10.1109/ACCESS.2024.343715412(106013-106024)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3437154
Song SHan JPark K(2024)A Data-Centric Contrastive Embedding Framework for Contextomized Quote DetectionIEEE Access10.1109/ACCESS.2024.337722712(40168-40181)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3377227
Nematirad RBehrang MPahwa A(2024)Acoustic-Based Online Monitoring of Cooling Fan Malfunction in Air-Forced Transformers Using Learning TechniquesIEEE Access10.1109/ACCESS.2024.336680712(26384-26400)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3366807
Ullah SHassan NBhatti NZia MShin J(2024)White balancing based improved nighttime image dehazingMultimedia Tools and Applications10.1007/s11042-024-18891-9Online publication date: 25-Mar-2024
https://doi.org/10.1007/s11042-024-18891-9
Jin YLi XWang JZhang YZhang M(2024)Raindrop Clarity: A Dual-Focused Dataset for Day and Night Raindrop RemovalComputer Vision – ECCV 202410.1007/978-3-031-72658-3_1(1-17)Online publication date: 2-Oct-2024
https://doi.org/10.1007/978-3-031-72658-3_1
Li CHe Y(2023)Nighttime Haze Image Restoration using Rolling Guidance Filter2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML)10.1109/ICICML60161.2023.10424741(92-96)Online publication date: 3-Nov-2023
https://doi.org/10.1109/ICICML60161.2023.10424741
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents