Abstract：It is difficult to model accurate uneven hazy image and solve residual problems during dehazing process. Therefore, an uneven hazy image dehazing method based on transmitted attention mechanism is proposed in this paper. Aiming at the heterogeneity of haze distribution, the transmitted attentions mechanism is designed in the network. The weight information in different modules can flow and cooperate to target and deal with the noise in the uneven hazy image. To reduce the loss of detail information caused by the common deep convolution, sparse smoothed dilated convolution is built to extract image features. Consequently, the receptive field is larger with more details retained. Finally, a lightweight residual block is utilized in parallel to supplement the color and detail information for the reconstructed image. Compared with mainstream methods, experiments on the uneven hazy image datasets and synthetic hazy image datasets show that the proposed method holds the advantages in subjective effects and objective evaluations.
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2022, Vol. 35 
