Image dehazing based on double branch convolution and detail enhancement

Because of detail loss, color distortion and contrast reduction in the image dehazing process in a haze condition, we proposed the image dehazing network based on double branch convolution and detail enhancement, which consists of image dehazing module and detail enhancement module. First, in the image dehazing module, we designed a double branch convolutional block based on depth-separable convolution and differential convolution and then combined it with the U-Net network, effectively reducing the detail loss in the image dehazing process. In the image dehazing model, we introduced the attention module composed of channel attention mechanism and pixel attention mechanism, which improves its feature extraction ability, suppresses the features that are not related to the current task and further reduces the color distortion and contrast in the image dehazing process. Then, we input the image dehazed with the image dehazing module into the detail enhancement module to further recover image details, so that the image is more similar to that in its real domain. The combination of the image dehazing module with the detail enhancement module improves the generalization ability of the image dehazing network and makes it more adaptable to the haze dataset. We carried out experiments with the public datasets ITS and Haze4K and the public real dataset IHAZE. The quantitative objective analysis and comparison show that the peak signal-to-noise ratio and the structural similarity index reach 39.69 dB and 0.994 respectively, indicating that there is a certain improvement compared with the optimal algorithm in the comparison network model. The subjective visual analysis shows that the image dehazed with the image dehazing network we proposed is more similar to the real no-haze image in terms of detail, color, contrast and so on.