  Single image dehazing is a critical image pre-processing step for subsequent
high-level computer vision tasks. However, it remains challenging due to its
ill-posed nature. Existing dehazing models tend to suffer from model
overcomplexity and computational inefficiency or have limited representation
capacity. To tackle these challenges, here we propose a fast and accurate
multi-scale end-to-end dehazing network called FAMED-Net, which comprises
encoders at three scales and a fusion module to efficiently and directly learn
the haze-free image. Each encoder consists of cascaded and densely connected
point-wise convolutional layers and pooling layers. Since no larger
convolutional kernels are used and features are reused layer-by-layer,
FAMED-Net is lightweight and computationally efficient. Thorough empirical
studies on public synthetic datasets (including RESIDE) and real-world hazy
images demonstrate the superiority of FAMED-Net over other representative
state-of-the-art models with respect to model complexity, computational
efficiency, restoration accuracy, and cross-set generalization. The code will
be made publicly available.
