  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.
