用于单图像去雾的隐式Euler ODE网络
Implicit Euler ODE Networks for Single-Image Dehazing
摘要：
深度卷积神经网络（CNN）已被应用于图像去雾任务，其中残差网络（ResNet）通常被用作基本组件以避免消失梯度问题。最近，许多工作表明，ResNet可以被认为是显式欧拉向前逼近的常微分方程（ODE）。在本文中，我们扩展了显式的前向近似的隐式向后对应，这可以通过递归神经网络，称为IM块实现。鉴于此，我们提出了一个有效的端到端的多级隐式网络（MI-Net）的单图像去雾问题。此外，多级融合（MLF）机制和剩余信道注意块（RCA块），以提高我们的网络的性能。在多个去雾基准数据集上的实验表明，该方法优于现有方法，达到了最先进的性能。
英文摘要：
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.
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