Image Defogging Algorithm Based on Attention Mechanism and Split Convolution.

In foggy days, the color saturation and contrast of the image are reduced due to factors such as scattering and refraction of light. Aiming at the problems of fog residue and loss of detailed features after processed by the existing dehazing methods, a defogging model network APSA-DehazeNet (Adaptive Pyramid Split Attention-DehazeNet) based on split convolution is proposed in this paper. Firstly, the adaptive multi-scale feature fusion module is used to extract the features of the foggy image, capture the features of different scales, then perform weighted fusion and obtain the shallow features of the image. Secondly, the deep features of the image are further obtained by the split convolutional network (PSANet) based on attention mechanism, and a more thorough dehazing effect is obtained. Finally, to effectively solve the problem of detail loss caused by a single loss, a joint loss function of perceived loss and structural similarity loss is proposed. Compared with other algorithms, experimental results demonstrate that the PSNR and SSIM indexes on the synthetic fog map dataset are improved by an average of 4.7dB and 7.4%, respectively. [ABSTRACT FROM AUTHOR]