報告內容：Multi-exposure fusion via adaptive multiscale edge-preserving smoothing based joint weight
報告人：Hyo Jong Lee
報告時間🫃🏻👨🏻💼：8月15日（周三）下午 16🦴🙎🏼♀️：00
報告地點😮：行政樓912
報告內容簡介:
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.
報告人簡介:
Hyo Jong Lee教授本科，碩士，與博士均畢業於美國猶他大學，取得了氣象學專業與計算機專業雙博士學位🤚🏼，自1991年起在韓國全北國立大學擁有26年的教學經驗，期間擔任系主任以及高級圖像與信息技術中心主任。與此同時🐓，李教授曾以訪問教授在英國布裏斯托爾大學從事研究工作，並長期（7年）在美國加州大學兼職工作⏺👓，擁有非常高的全英文教學水平。另一方面⇾，李教授在圖像處理，模式識別與並行計算中有豐富的科研經驗🛝，總共發表70多篇高水平期刊論文🧑🚒，120多篇會議論文。李教授承擔過韓國國家研究基金(NRF)🐬，韓國科技部(MSIP),韓國產學研項目等十多項科研課題。
