A Multistage with Multiattention Network for Single Image Dehazing
Bin Hu, Mingcen Gu, Yuehua Li · Scientific Programming · 2022
For single image dehazing, an end-to-end multistage with multiattention network is proposed in this paper. The network contains two different stages, in which the first stage uses an encoder-decoder subnet to obtain contextual features, and the second stage adopts a single-scale pipeline to provide spatial image details. At each stage, ground-truth supervision is provided, and an attention mechanism is used between the two stages, so the features learned from the previous stage will be refined before passing to the next stage. A basic multiattention unit that combines channel attention, spatial attention, and pixel attention is designed to earn more weight from important features, and a positional normalization that normalizes exclusively across channels is used in the multiattention unit to learn more weight from important features. Experimental results in several benchmarks indicate that the proposed network outperforms the state-of-the-art methods both quantitatively and qualitatively.