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.
Fund:Supported by National Key R&D Program(No.2018YFC060450 2), Science and Technology Project of Henan Province(No.212 102210390,192102210100), Support Project of Intelligent Coal Mining Technology Innovation Center of Henan Province(No.20 21YD01)
Corresponding Authors:YANG Yi, Ph.D., associate professor. His research interests include artificial intelligence and machine vision
About author:: About Author:WANG Keping, Ph.D., associate profe-ssor. Her research interests include image dehazing and target detection and tracking.
DUAN Yumeng, master student. Her research interests include image dehazing and deep learning.
FEI Shumin, Ph.D., professor. His research interests include nonlinear control system design and synthesis, neural network control and delay system control.
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2022, Vol. 35 
