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• W3040694766 abstract "Deep convolutional neural networks (CNN) have been applied for image dehazing tasks, where the residual network (ResNet) is often adopted as the basic component to avoid the vanishing gradient problem. Recently, many works indicate that the ResNet can be considered as the explicit Euler forward approximation of an ordinary differential equation (ODE). In this paper, we extend the explicit forward approximation to the implicit backward counterpart, which can be realized via a recursive neural network, named IM-block. Given that, we propose an efficient end-to-end multi-level implicit network (MI-Net) for the single image dehazing problem. Moreover, multi-level fusing (MLF) mechanism and residual channel attention block (RCA-block) are adopted to boost performance of our network. Experiments on several dehazing benchmark datasets demonstrate that our method outperforms existing methods and achieves the state-of-the-art performance." @default.
• W3040694766 created "2020-07-16" @default.
• W3040694766 creator A5051008303 @default.
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• W3040694766 date "2020-07-13" @default.
• W3040694766 modified "2023-09-28" @default.
• W3040694766 title "Implicit Euler ODE Networks for Single-Image Dehazing" @default.
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• W3040694766 doi "https://doi.org/10.48550/arxiv.2007.06443" @default.
• W3040694766 hasPublicationYear "2020" @default.
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