指导内容：Deep Learning for Visibility Restoration Approach
指导人：台北科技大学黄士嘉教授
指导时间：4月3日16:30
指导地点：行政楼912
讲座内容简介:
The visibility of outdoor images captured in inclement weather is often degraded due to the presence of haze, fog, sandstorms, and snowfall, so on. Poor visibility caused by atmospheric phenomena in turn causes failure in computer vision applications, such as outdoor object recognition systems, obstacle detection systems, video surveillance systems, and intelligent transportation systems. In order to solve this problem, visibility restoration techniques have been developed and play an important role in many computer vision applications that operate in various weather conditions.In this talk, we will introduce two efficient atmospheric particle removal approaches: 1) rule-based haze removal approach, and 2) learning-based snow removal approach.The rule-based atmospheric particle removal approaches are designed with strong assumptions regarding spatial frequency, trajectory, and translucency and the learning-based snow removal approaches are more complicated because they possess additional attributes of particle size and shape, and these attributes may vary within a single image.
