Abstract
While deep learning-based dehazing methods have achieved significant success in recent years, most emphasize more on dehazing and less on image color recovery. In this paper, we propose a progressive network incorporating color layers. It gradually recovers the image by repeatedly invoking an auxiliary progressive network. The RGBA image information captured by the soft color segmentation is used as the input for the auxiliary learning. Specifically, we first introduce the gated recurrent unit in the feature extraction module, which can effectively extract image features while preventing model overfitting. Next, local features are extracted in the residual learning module by combining the recurrent layer and residual blocks. Finally composite module integrates the features to produce a clean image with rich details. In addition, recursive computation is used in each stage to reduce network parameters while improving performance. Extensive experimental results demonstrate that the proposed method outperforms the state-of-the-arts quantitatively and qualitatively.
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References
Akimoto N, Zhu H, Jin Y, Aoki Y (2020) Fast soft color segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 8277–8286
Akimoto N, Zhu H, Jin Y, Aoki Y (2020) Fast soft color segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 8277–8286
Aksoy Y, Aydin TO, Smolić A, Pollefeys M (2017) Unmixing-based soft color segmentation for image manipulation. ACM Transactions on Graphics (TOG) 36:1–19
Aksoy Yaġiz, Oh Tae-Hyun, Paris S, Pollefeys M, Matusik W (2018) Semantic soft segmentation. ACM Transactions on Graphics (TOG) 37:1–13
Anvari Z, Athitsos V (2020) Dehaze-GLCGAN:, unpaired single image de-hazing via adversarial training, arXiv:2008.06632
Cai B, Xu X, Jia K, Qing C, Tao D (2016) Dehazenet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25:5187–5198
Chen W-T, Fang H-Y, Ding J-J, Kuo S-Y (2020) PMHLD: Patch Map Based Hybrid Learning DehazeNet For Single Image Haze Removal. IEEE Transactions on Image Processing
Chen D, He M, Fan Q, Liao J, Zhang L, Hou D, Yuan L, Hua G (2019) Gated context aggregation network for image dehazing and deraining. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) 1375–1383
Dharejo FA, Zhou Y, Deeba F, Du Y (2019) A color enhancement scene estimation approach for single image haze removal. IEEE Geoscience and Remote Sensing Letters
Dong H, Pan J, Xiang L, Hu Z, Zhang X, Wang F, Yang M-H (2020) Multi-Scale Boosted dehazing network with dense feature fusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp 2157–2167
Fattal R (2008) Single image dehazing. ACM transactions on graphics (TOG) 27:1–9
Fries R, Modestino J (1979) Image enhancement by stochastic homomorphic filtering. IEEE Transactions on Acoustics Speech, and Signal Processing 27:625–637
He K, Sun J, Tang X (2010) Single image haze removal using dark channel prior. IEEE transactions on Pattern Analysis and Machine Intelligence 33:2341–2353
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European conference on computer vision, pp 694–711
Khatun A, Haque MR, Basri R, Uddin MS et al (2020) Single image dehazing: an analysis on generative adversarial network. Journal of Computer and Communications 8:127
Kim J-Y, Kim L-S, Hwang S-H (2001) An advanced contrast enhancement using partially overlapped sub-block histogram equalization. IEEE Transactions on Circuits and Systems for Video Technology 11:475–484
Kinoshita Y, Kiya H (2020) Hue-Correction Scheme based on Constant-Hue plane for Deep-Learning-Based Color-Image enhancement. IEEE Access 8:9540–9550
Koyama Y, Goto M (2018) Decomposing images into layers with advanced color blending, 397– 407
Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690
Li J, Feng X, Hua Z (2021) Low-light image enhancement via progressive-recursive network. IEEE Transactions on Circuits and Systems for Video Technology
Li J, Li G, Fan H (2018) Image dehazing using residual-based deep CNN. IEEE Access 6:26831–26842
Li B, Peng X, Wang Z, Xu J, Feng D (2017) An all-in-one network for dehazing and beyond, arXiv:1707.06543
Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2017) Reside:, A benchmark for single image dehazing, arXiv:1712.04143, 1
Li M-W, Wang Y-T, Geng J, Hong W-C (2021) Chaos cloud quantum bat hybrid optimization algorithm. Nonlinear Dynamics 103(1):1167–1193
Li B, Zhao J, Fu H (2020) DLT-Net: deep learning transmittance network for single image haze removal. In: Signal Image and Video Processing, pp 1–9
Li L, Zhou Y, Wu J, Qian J, Chen B (2016) Color-Enriched Gradient similarity for retouched image quality evaluation. IEICE Trans Inf Syst 99:773–776
Liu X, Ma Y, Shi Z, Chen J (2019) Griddehazenet: Attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE International Conference on Computer Vision, pp 7314– 7323
Liu Y, Zhao G (2018) Pad-net:, A perception-aided single image dehazing arXiv:1805.03146
Lou W, Li Y, Yang G, Chen C, Yang H, Yu T (2020) Integrating haze density features for fast nighttime image dehazing. IEEE Access 8:113318–113330
Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization, 617–624. In: Proceedings of the IEEE international conference on computer vision Proceedings of the IEEE international conference on computer vision
Narasimhan SG, Nayar SK (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, pp 598–605
Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Transactions on Pattern Analysis and Machine Intelligence 25:713–724
Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch
Peng Y-T, Lu Z, Cheng F-C, Zheng Y, Huang S-C (2019) Image Haze Removal Using Airlight White Correction, Local light filter, and aerial perspective prior. IEEE Trans Circuits Syst Video Technol 30:1385–1395
Qin X, Wang Z, Bai Y, Xie X, Jia H (2020) FFA-net: Feature fusion attention network for single image dehazing. In: AAAI, pp 11908–11915
Ren W, Pan J, Zhang H, Cao X, Yang M-H (2020) Single image dehazing via multi-scale convolutional neural networks with holistic edges. Int J Comput Vis 128:240–259
Ren D, Zuo W, Hu Q, Zhu P, Meng D (2019) Progressive image deraining networks: a better and simpler baseline. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3937–3946
Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: European conference on computer vision, pp 746–760
Stark JA (2000) Adaptive image contrast enhancement using generalizations of histogram equalization. IEEE Trans Image Process 9:889–896
Tai Y-W, Jia J, Tang C-K (2007) Soft color segmentation and its applications. IEEE Transactions on Pattern Analysis and Machine Intelligence 29:1520–1537
Tan RT (2008) Visibility in bad weather from a single image. 2008 IEEE Conference on Computer Vision and Pattern Recognition, 1–8
Tan J, Echevarria J, Gingold Y (2018) Efficient palette-based decomposition and recoloring of images via RGBXY-space geometry. ACM Transactions on Graphics (TOG) 37:1–10
Tan J, Echevarria J, Gingold Y (2018) Efficient palette-based decomposition and recoloring of images via RGBXY-space geometry. ACM Transactions on Graphics (TOG) 37:1–10
Tangsakul S, Wongthanavasu S (2020) Single image haze removal using deep cellular automata learning. IEEE Access 8:103181–103199
Wang JB, He N, Ke L (2015) A new single image dehazing method with MSRCR algorithm. In: Proceedings of the 7th International Conference on Internet Multimedia Computing and Service , pp 1–4
Wang Z, Yang Y (2018) A non-iterative clustering based soft segmentation approach for a class of fuzzy images. Appl Soft Comput 70:988–999
Wu Q, Zhang J, Ren W, Zuo W, Cao X (2019) Accurate transmission estimation for removing haze and noise from a single image. IEEE Trans Image Process 29:2583–2597
Xie B, Guo F, Cai Z (2010) Improved single image dehazing using dark channel prior and multi-scale retinex. 2010 International Conference on Intelligent System Design and Engineering Application 1:848–851
Yamak PT, Yujian L, Gadosey PK (2019) A Comparison between ARIMA, LSTM, and GRU for Time Series Forecasting. In: Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, pp 49–55
Zhang W, Dong L, Pan X, Zhou J, Qin L, Xu W (2019) Single image defogging based on multi-channel convolutional MSRCR. IEEE Access 7:72492–72504
Zhang Z, Hong W-C (2021) Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl-Based Syst 228: 107297
Zhang T, Yang X, Wang X, Wang R (2020) Deep joint neural model for single image haze removal and color correction. Information Sciences
Zhao S, Zhang L, Huang S, Shen Y, Zhao S (2020) Dehazing evaluation: Real-world Benchmark Datasets. In: Criteria and Baselines, IEEE Transactions on Image Processing
Zhou J, Zhou F (2013) Single image dehazing motivated by Retinex theory. 2013 2nd international symposium on instrumentation and measurement, sensor network and automation (IMSNA) 243– 247
Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24:3522–3533
Acknowledgments
The authors acknowledge the National Natural Science Foundation of China (61772319, 62002200, 61976125 and 61976124), and Shandong Natural Science Foundation of China (ZR2020QF012 and ZR2021MF068).
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Li, X., Hua, Z. & Li, J. Color layers -Based progressive network for Single image dehazing. Multimed Tools Appl 81, 32755–32778 (2022). https://doi.org/10.1007/s11042-022-12731-4
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DOI: https://doi.org/10.1007/s11042-022-12731-4