Abstract
In recent years, the researchers of image dehazing mainly focused on deep learning algorithms. However, due to the defective network structure, and inadequate feature extraction, the deep learning algorithm still has many problems to be solved. In this paper, we fuse the physical models including haze imaging model with absorption compensation, multiple scattering imaging model and multi-scale retinex imaging model with convolutional neural network to construct the image dehazing network. Multiple scattering haze imaging model is used to describe the haze imaging process in a more consistent way with the physical imaging mechanism. And the multi-scale retinex imaging model ensures the color fidelity. In the network structure, multi-scale feature extraction module can improve network performance in terms of feature reuse. In the attention feature extraction module, the back-propagating of the important front features is used to enhance features. This method can effectively make up for the deficiency that autocorrelation features cannot share the deep-level information, which is also effective for features replenishment. The results of the comparative experiment demonstrate that our network outperforms state-of-the-art dehazing methods.
Supported by Liaoning Education Department General Project Foundation (LJKZ0231); Huaian Natural Science Research Plan Project Foundation (HAB202083).
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Cui, T., Zhang, M., Ge, S., Chen, X. (2022). Research on Multi-model Fusion Algorithm for Image Dehazing Based on Attention Mechanism. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_47
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