文献类型：期刊文献

英文题名：Method for coloring night-vision imagery based on multispectral semantic segmentation

作者：Zhang, Weiwen[1]; Gu, Xiaojing[1]; Gu, Xingsheng[1]

机构：[1] Key Laboratory of Advanced Control and Optimization for Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China

年份：2018

起止页码：104

外文期刊名：ANZCC 2018 - 2018 Australian and New Zealand Control Conference

收录：EI(收录号：20191006598152)

语种：英文

外文关键词：Deep learning - Table lookup - Image enhancement - Semantic Segmentation - Semantics - Color image processing - Vision - Infrared radiation

摘要：Color night vision can map natural colors to nighttime images of multiple bands (e.g., visible and long-wave infrared (LWIR)). These colors can assist the observers in better and faster understanding images, thus improving their situational awareness and shortening the reaction time. In this paper, we present an effective method combining deep learning and category colors. It utilizes the semantic segmentation for image segmentation first, and then colorize the image according to categories to avoid the same color scheme and unnatural colors. We compare our method with some others quantitatively and qualitatively, such as global colorization by single lookup table, where we show significant improvements. In addition, it can be expanded according to different environments and applications because of the fixed category colors. ? 2018 IEEE.