报告时间： 2017年11月14日 周二 15:30
报告地点： 湖南大学 信息科学与工程学院 542 报告厅
报告人概况：朱贤益，湖南大学信息科学与工程学院2016级博士生，导师为肖懿教授。主要研究兴趣为三维建模，图像语义，深度学习。目前已有成果发表在国际期刊Multimedia Tools and Applications和国际会议International Conference on Virtual Reality and Visualization以及 International Workshop on Trust, Security and Privacy for Big Data。
报告主要内容：Inverse halftoning is a kind of technology which transforms binary images composed of black and white pixels to continuous-tone images. Many scholars have studied this problem so far, but the results are not satisfactory. In this paper, we propose an end-to-end deep convolutional neural network composed of two parts. The first part is the feature extraction part which consists of 4 convolution layers and 4 pooling layers to extract feature from the halftoning images. The second part is the reconstruction part which contains 4 deconvolution layers to reconstruct the continuous-tone images. A U-Net structure which concatenates the outputs from the feature extraction layers with deconvolution layers is used for better restoring the detail information of the original images. Experimental results show that our method outperforms the state-of-arts in terms of both visual quality and numerical evaluation.