Super-resolution for semantic segmentation

Image segmentation is a classical problem in the field of computer vision. With the extensive development of deep learning, it has achieved much progress in semantic segmentation. However, the mainstream networks used in deep learning such as Fast-SCNN, U-Net, which still face challenges in image segmentation. A common problem is that linear interpolation is used in the up-sampling stage of these networks to obtain high-resolution images. Due to the lack of sufficient feature information, the contours of the objects in the image are blurred and grided. For this reason, we propose a new super-resolution (SR) method to replace the up-sampling with linear interpolation in the network model. Five representative networks integrated with our proposed SR module are used for verification on the CamVid data set. The experimental results show that our method has a 2%~4% improvement in mIoU (the mean value of Intersection over Union) and a 2%~3% improvement in pixel accuracy, which demonstrates its generalization and effectiveness of our method.