Abstract
Application scenarios such as unmanned driving and UAV reconnaissance have the requirements of high performance, low delay and small space occupation. Images taken in foggy days are easy to be affected by fog or haze, thus losing some important information. The purpose of image dehazing is to remove the influence of fog on image quality, which is of great significance to assist in solving high-level vision tasks. Aiming at the shortcomings of the current defogging method, such as slow defogging speed and poor defogging effect, this paper introduces the idea of FPCNet and the attention mechanism module, and proposes an improved AODNet fast defogging algorithm to ensure the defogging speed and defogging performance. The public dataset RESIDE was used for training and testing. Experimental results show that in terms of dehazing performance, the proposed algorithm achieves 25.78 and 0.992 in PSNR and SSIM respectively. In terms of dehazing speed, the proposed method is close to AODNet, with only 5 times more parameters than AODNet, but more than 100 times smaller than other methods.
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References
Xie, Y., Jia, H., Wang, T., Lei, C., Xu, K., Chen, Q.: Review of image dehazing algorithms∗. Comput. Dig. Eng. 50(12), 2765–2774 (2022)
Wu, D., Zhu, Q.: Research progress of image dehazing. Acta Automatica Sinica 41(2), 221–239 (2015)
Jiang, H., Yang, Z., Zhang, X.: Research progress of image dehazing algorithm. J. Jilin Univ. Eng. Technol. Ed. 51(4), 1169–1181 (2021)
Li, X.: Image enhancement algorithm Based on Retinex theory. Comput. Appl. Res. 22(2), 235–237 (2005)
Chen, S., Cao, S., Cui, M.Y.: Blind image deblurring algorithm based on deep multilevel wavelet transform. J. Electron. Inf. Technol. 43(1), 1–8 (2021)
Ma, X.: Ship image enhancement method based on homomorphic filtering. Ship Sci. Technol. 42(6), 89–91 (2020)
Fattel, R.: Single image dehazing. ACM Trans. Graph. 27(3), 721–728 (2008)
He, K., Sun, J., Tang, X.O.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)
Meng, G.F., Wang, Y., Duan, J.H., et al.: Efficient image dehazing with boundary constraint and contextual regularization. In: IEEE International Conference on Computer Vision, pp. 617–624 (2013)
Berman, D., Treibitz, T., Avidan, S.: Non-local image dehazing. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
Zhong, L., Shang, Y., Zhou, X., et al.: Fast single image dehazing based on a regression model. Neurocomputing 245, 10–22 (2017)
Tarel, J.-P., Hautiere, N.: Fast visibility restoration from a single color or gray level image. In: IEEE 12th International Conference on Computer Vision, pp. 2201–2208 (2009)
Zhang, D., Wu, P.: Fast dehazing algorithm for single image. Comput. Eng. Appl. 55(10), 213–217 (2019)
Cai, B., Xu, X., Jia, K., et al.: DehazeNet: an end-to-end system for single image haze removal. IEEE Trans. Image Process. 25(11), 5187–5198 (2016)
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: Aod-net: all-in-one dehazing network. In: IEEE International Conference on Computer Vision, pp. 4770–4778 (2017)
Ren, W., Liu, S., Zhang, H., et al.: Single image dehazing via multi-scale convolutional neural networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) Computer Vision-ECCV, pp. 154–169. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-319-46475-6_10
Ju, Q., Li, C., Sang, Q.: Single image dehazing method based on improved multi-scale convolutional Neural Network. Comput. Eng. Appl. 55(10), 179–185 (2019)
Mei, K., Jiang, A., Li, J., et al.: Progressive feature fusion network for realistic image dehazing (2018)
Qin, X., Wang, Z., Bai, Y., et al.: FFA-net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)
Zhang, J., Tao, D.: FAMED-net: a fast and accurate multi-scale end-to-end dehazing network. IEEE Trans. Image Process. 29, 72–84 (2019)
Tran, L.-A., Moon, S., Park, D.-C.: A novel encoder-decoder network with guided transmission map for single image dehazing. Procedia Comput. Sci. 204, 682–689 (2022)
Zhang, J., Cao, Y., Wang, Y., Zha, Z.J., Wen, C., Chen, C.W.: Fully point-wise convolutional neural network for modeling statistical regularities in natural images. ArXiv (2018)
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Chen, S., Liu, S., Chen, X., Dan, J., Wu, B. (2024). Improved AODNet for Fast Image Dehazing. In: Wu, C., Chen, X., Feng, J., Wu, Z. (eds) Mobile Networks and Management. MONAMI 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-55471-1_12
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