Densely connected convolutional transformer for single image dehazing

Image Dehazing is an important low-level vision task that aims to remove the haze from an image. In this paper, we proposed Densely Connected Convolutional Transformer (DCCT) for single image dehazing. DCCT is an efficient architecture that combines the multi-head Performer with the local dependencies. To prevent loss of information between features at different levels, we propose a learnable connection layer that is used to fuse features at different levels across the entire architecture. We guide the training of DCCT through a joint loss considering a supervised metric learning approach that allows us to consider both negative and positive features for a multi-image perceptual loss. We validate the design choices and the effectiveness of the proposed DCCT through ablation studies. Through comparison with the representative techniques, we establish that the proposed DCCT is highly competitive with the state of the art.