Although deep neural networks have made significant progress in semantic segmentation, speed and computational cost still can't meet the strict requirements of real-world applications. In this paper, we present an enhanced asymmetric convolution network (EACNet) to seek a balance between accuracy and speed. Specifically, we design a pair of enhancing asymmetric convolution modules constructed by depth-wise asymmetric convolution and dilated convolution to extract short-range and longrange features, which are efficient and powerful. Additionally, we apply a bilateral structure in which the detail branch preserves low-level spatial details while the semantic branch captures high-level context information. The two branches are merged at different stages of the network to strengthen information propagation between different levels. The experiments on the Cityscapes dataset show that our method achieves high accuracy and speed with relatively small parameters. Compared with other real-time semantic segmentation methods, our network attains a good trade-off among parameters, speed, and accuracy.

The authors propose a new single image dehazing method. Different from image restoration and image enhancement method, their method is based on the idea of image fusion. Image dehazing is to remove the influence of the haze between the scene and the camera. First, combined with the depth information, the haze layer is subtracted in the hazy image to improve the colour saturation, which produces the first input image. Then, the gamma correction is used on the grey image. Second, the details of the gamma correction image are enhanced to produce the second input image. Finally, the two input images are fused by local linear model to obtain the final restored image. Experimental results show that the restored image has high contrast, rich details, and without colour distortion in the sky area.