Abstract
This paper proposed a multi-scale feature fusion image dehazing network by incorporating a contiguous memory mechanism (MFFDN-CM). Specifically, the pixel attention mechanism, continuous memory strategy and residual dense blocks are integrated into the dehazing model with a prevalent encoder-decoder structure (U-Net). Firstly, our model obtains multiscale feature maps by subsampling operations, and further employs skip connections between the corresponding network layers to connect the feature maps between the encoder and the decoder for good feature fusion. Then, we introduce a continuous memory residual block to strengthen the information flows for feature reuse. Moreover, to leverage detail representation and accomplish adaptive dehazing according to the haze density, MFFDN-CM adopts a pixel attention module on the skip connections to combine the residual dense block module of the corresponding decoding layers. Finally, multiple residual blocks are exploited on the bottleneck in encoder-decoder structure to prevent network performance degradation due to vanishing gradients. Experimental results demonstrate the proposed model can achieve better hazing performance than the state-of-the-art methods based on deep neural network.
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References
Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., Yang, M.-H.: Single image dehazing via multi-scale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 154–169. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46475-6_10
Cai, B., et al.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)
He, K., Jian, S., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)
Zhang, H., Patel, M.V.: Densely connected pyramid dehazing network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Yang, D., Sun, J.: Proximal Dehaze-Net: a prior learning-based deep network for single image dehazing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 729–746. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_43
Li, B., et al.: AoD-Net: all-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision (2017)
Li, Z., et al.: Simultaneous video defogging and stereo reconstruction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015)
Chen, D., et al.: Gated context aggregation network for image dehazing and deraining. In:Â IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE (2019)Â
Engin, D., Genç, A., Ekenel, H.K.: Cycle-Dehaze: enhanced cycleGAN for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2018)
Liu, X., et al.: Dual residual networks leveraging the potential of paired operations for image restoration. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
Chen, Y., et al.: Dual path networks. In: Advances in Neural Information Processing Systems, vol. 30 (2017)
Liu, X., et al.: GridDehazeNet: attention-based multi-scale network for image dehazing. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (2019)
Mei, K., Jiang, A., Li, J., Wang, M.: Progressive feature fusion network for realistic image dehazing. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11361, pp. 203–215. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20887-5_13
Ren, W., et al.: Gated fusion network for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Zhang, Y., et al.: Residual dense network for image super-resolution. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
Qin, X., et al.: FFA-Net: feature fusion attention network for single image dehazing. In:Â Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 7 (2020)
Xiang, L., Dong, H., Wang, F., Guo, Y., Ma, K.: Gated contiguous memory U-Net for single image dehazing. In: Gedeon, T., Wong, K.W., Lee, M. (eds.) ICONIP 2019. LNCS, vol. 11954, pp. 117–127. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-36711-4_11
Li, B., et al.: Benchmarking single-image dehazing and beyond. IEEE Trans. Image Process. 28(1), 492–505 (2018)
Meng, G., et al.: Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE International Conference on Computer Vision (2013)
Liu, Q., et al.: Single image dehazing with depth-aware non-local total variation regularization. IEEE Trans. Image Process. 27(10), 5178–5191 (2018)
Feng, T., et al.: URNet: a U-Net based residual network for image dehazing. Appl. Soft. Comput. 102, 106884 (2021)
Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28
He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)
He, K., et al.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision (2015)
Wu, Y., He, K.: Group normalization. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11217, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01261-8_1
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)
Shao, Y., et al.: Domain adaptation for image dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
Liu, Y., et al.: From synthetic to real: image dehazing collaborating with unlabeled real data. In: Proceedings of the 29th ACM International Conference on Multimedia (2021)
Acknowledgements
This paper is supported by the National Nature Science Foundation of China (No. 61861020), the Jiangxi Province Graduate Innovation Special Fund Project (No. YC2021-X06) and the Nanchang Educational Big Data & Intelligent Technology Key Laboratory (No. 2020NCZDSY-012).
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Li, Q., Xie, Z., Zong, S., Liu, G. (2022). Image Dehazing Based on Deep Multiscale Fusion Network and Continuous Memory Mechanism. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Methodologies. ICIC 2022. Lecture Notes in Computer Science(), vol 13395. Springer, Cham. https://doi.org/10.1007/978-3-031-13832-4_34
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