Local Blurred Natural Image Restoration Based on SelfReference Deblurring Generative Adversarial Networks This paper solves the deblurring problem of local blurred natural images by the proposed selfreference deblurring generative adversarial networks, that is SRDeblur GAN. The method proposed divides the clear part and the blurred part of picture, and guides the deblurring process of the blurred part with the former. By the novel network architecture and a multicomponent loss function, the method achieves better deblurring results both on artificial local blurred images and natural local blurred images than the recently most advanced deblurring generative adversarial networks, i.e. Deblur GAN, obtaining the stateoftheart. The network can also have application to local inpainting and local translation for images.

Unconstrained Road Marking Recognition with Generative Adversarial Networks Recent road marking recognition has achieved great success in the past few years along with the rapid development of deep learning. Although considerable advances have been made, they are often overdependent on unrepresentative datasets and constrained conditions. In this paper, to overcome these drawbacks, we propose an alternative method that achieves higher accuracy and generates highquality samples as data augmentation. With the following two major contributions: 1) The proposed deblurring network can successfully recover a clean road marking from a blurred one by adopting generative adversarial networks (GAN). 2) The proposed data augmentation method, based on mutual information, can preserve and learn semantic context from the given dataset. We construct and train a classconditional GAN to increase the size of training set, which makes it suitable to recognize target. The experimental results have shown that our proposed framework generates deblurred clean samples from blurry ones, and outperforms other methods even with unconstrained road marking datasets.