Sequentially Refined Spatial and Channel-Wise Feature Aggregation in Encoder-Decoder Network for Single Image Dehazing

Single image haze removal is a challenging problem due to its inherent ill-posed nature. Several prior-based and learning-based methods have been proposed to solve this problem and they have achieved superior results. However, the performance of prior-based methods is limited by hand-designed features. Meanwhile, the learning-based methods have flaws in spatial correlation. Because they just employ the convolutional neural network (CNN) as an end-to-end mapping module, generating dehazed image directly or learning parameters in atmospheric scattering model, without associating the relationship between feature activation and distribution of haze. Instead of using prior or CNN to simply estimate parameters of atmospheric scattering model, we propose a sequentially refined Spatial and Channel-wise Feature Aggregation (SCFA) dehazing network, called SCFADN. Specifically, our network learns residues between clean images and hazy images through an improved encoder-decoder network which incorporated the proposed sequentially refined SCFA module. This module efficiently learns long-range feature correlation for dehazing residue modeling, which enables the shallow network removing haze effectively. Extensive experiments on synthetic and real datasets results demonstrate that the proposed method achieves superior performance over the state-of-the-art methods.