报告内容：Multi-exposure fusion via adaptive multiscale edge-preserving smoothing based joint weight
报告人：Hyo Jong Lee
报告时间：8月15日（周三）下午 16：00
报告地点：行政楼912
报告内容简介:
Natural scenes usually have a larger dynamic range than the dynamic range that can be acquired by an optical camera with a single shot. In this talk, we propose a multi-exposure fusion method that effectively fuses in a direct manner differently exposed images of a high dynamic range scene into a high-quality image. First, we present a developed joint weight by considering the exposure level measurement of local and global luminance components of the input images. Second, we introduce a designed multiscale edge-preserving smoothing (MEPS) model for direct representing the weight maps. Third, two scale-aware factors for the MEPS model are adaptively determined without manual interference to obtain an optimal representation effect for each scale of the weight maps. The proposed adaptive MEPS model does not require Gaussian filtering steps to first smooth the weight maps. It significantly reduces spatial artifacts in the fused image.
报告人简介:
Hyo Jong Lee教授本科，硕士，与博士均毕业于美国犹他大学，取得了气象学专业与计算机专业双博士学位，自1991年起在韩国全北国立大学拥有26年的教学经验，期间担任系主任以及高级图像与信息技术中心主任。与此同时，李教授曾以访问教授在英国布里斯托尔大学从事研究工作，并长期（7年）在美国加州大学兼职工作，拥有非常高的全英文教学水平。另一方面，李教授在图像处理，模式识别与并行计算中有丰富的科研经验，总共发表70多篇高水平期刊论文，120多篇会议论文。李教授承担过韩国国家研究基金(NRF)，韩国科技部(MSIP),韩国产学研项目等十多项科研课题。
