  Convolutional neural networks (CNNs) show outstanding performance in many
image processing problems, such as image recognition, object detection and
image segmentation. Semantic segmentation is a very challenging task that
requires recognizing, understanding what's in the image in pixel level. Though
the state of the art has been greatly improved by CNNs, there is no explicit
connections between prediction of neighbouring pixels. That is, spatial
regularity of the segmented objects is still a problem for CNNs. In this paper,
we propose a method to add spatial regularization to the segmented objects. In
our method, the spatial regularization such as total variation (TV) can be
easily integrated into CNN network. It can help CNN find a better local optimum
and make the segmentation results more robust to noise. We apply our proposed
method to Unet and Segnet, which are well established CNNs for image
segmentation, and test them on WBC, CamVid and SUN-RGBD datasets, respectively.
The results show that the regularized networks not only could provide better
segmentation results with regularization effect than the original ones but also
have certain robustness to noise.

Computer Vision and Pattern Recognition