Transformer-based progressive residual network for single image dehazing

The seriously degraded fogging image affects the further visual tasks. How to obtain a fog-free image is not only challenging, but also important in computer vision. Recently, the vision transformer (ViT) architecture has achieved very efficient performance in several vision areas.

In this paper, we propose a new transformer-based progressive residual network. Different from the existing single-stage ViT architecture, we recursively call the progressive residual network with the introduction of swin transformer. Specifically, our progressive residual network consists of three main components: the recurrent block, the transformer codecs and the supervise fusion module. First, the recursive block learns the features of the input image, while connecting the original image features of the original iteration. Then, the encoder introduces the swin transformer block to encode the feature representation of the decomposed block, and continuously reduces the feature mapping resolution to extract remote context features. The decoder recursively selects and fuses image features by combining attention mechanism and dense residual blocks. In addition, we add a channel attention mechanism between codecs to focus on the importance of different features.

The experimental results show that the performance of this method outperforms state-of-the-art handcrafted and learning-based methods.