Date & Time: 

2022 / 11 / 25 (Fri) 14:20 - 16:20

Location: 

Delta Building R216, NTHU

Speaker: 

 林家祥 教授

國立成功大學 電機工程學系

Topic: 

CODE-iOS: Convex Optimization and Deep Learning Based Imaging for Optical Satellite

Abstract: 

Convex optimization (CO) and deep learning (DE) are the most powerful/popular theoretical frameworks for solving inverse problems. However, CO often induces a math-heavy optimization procedure, which is a daunting task for most software developers. Also, DE usually requires collecting big data, quite expensive or even unavailable. Motivating by these facts, we invent a new inverse imaging theoretical framework, termed CODE. CODE, as its name suggested, blends the advantages of CO and DE, thereby allowing us to solve challenging inverse problems using just small data and regularizers of very simple math form. We bridge CO and DE using the so-called Q-quadratic norm—a simple convex regularizer for extracting key features embedded in a weak DE solution learned from small data (or even single data). The radically new CODE theory achieves state-of-the-art performance in reconstructing NASA's highly damaged hyperspectral satellite images. If time permits, I will also share our recent research results about quantum satellite image processing.

Biography: 

林家祥副教授2010於清大電機系取得學士學位、2016於清大通訊所取得博士學位。現由成大電機系主聘、敏求智慧運算學院合聘。曾於香港中文大學(2014)、美國維吉尼亞理工(2015-2016)、葡萄牙里斯本大學(2017-2018)、中央大學太空遙測中心(2018)，鑽研通訊、生醫、太空領域之研究，近期研究領域涵蓋凸優化、盲訊號處理、深度學習、衛星遙測、量子影像處理。曾榮獲五年期(2018-2023)科技部「愛因斯坦計畫」、2020 IEEE地科與遙測學會「Prize Paper Award」、2021科技部電信學門計畫執行成果「特優獎」、2021 IEEE Tainan Section「最佳年輕學者獎」、2022國家科學及技術委員會「FutureTech Award」（未來科技獎）、2022中國電機工程學會「優秀青年電機工程師獎」。