Yaochu Jin, Westlake University, China

IEEE Fellow, Member of Academia Europaea


Yaochu Jin (Fellow, IEEE) received the B.Sc., M.Sc., and Ph.D. degrees in automatic control from Zhejiang University, in 1988, 1991 and 1996, respectively, and the Dr.Ing, degree in neuroinformatics from Ruhr University Bochum, Germany in 2001. He joined the Westlake University in October 2023 as a Chair Professor of AI, leading the Trustworthy and General Al Laboratory. He was an Alexander von Humboldt Professor for Al with the Faculty of Technology, Bielefeld University, Germany from 2021 to 2023, and a Distinguished Chair, Professor in Computational Intelligence with the Department of Computer Science, University of Surrey, UK. He was a Finland Distinguished Professor with the University of JyvAskyla, Finland, Changjiang Distinguished Visiting Professor with Northeastern University, China, and Distinguished Visiting Scholar, University of Technology Sydney, Australia. His main research interests include multi-objective and data-driven evolutionary optimization, evolutionary multi-objective learning, trustworthy AI, and evolutionary developmental AI. Professor Jin is presently the President of the IEEE Computational Intelligence Society and the Editor-in-Chief of Complex & Intelligent Systems. He is the recipient of the 2018, 2021 and 2023 IEEE Transactions on Evolutionary Computation Outstanding Paper Award, and the 2015, 2017, and 2020 IEEE Computational Intelligence Magazine Outstanding Paper Award. He was named by the Clarivate as a Highly Cited Researcher from 2019 to 2023 consecutively. He is a Member of Academia Europaea and Fellow of IEEE.


KWONG Tak Wu Sam, Lingnan University Hong Kong, China

Fellow of Hong Kong AES, US NAI and IEEE


Sam Kwong received his B.Sc. degree from the State University of New York at Buffalo, M.A.Sc. in electrical engineering from the University of Waterloo in Canada, and Ph.D. from Fernuniversität Hagen, Germany.  Kwong is currently a Chair Professor at the Lingnan University, Department of Comping and Decision Science. Prof. Kwong is the associate editor of leading IEEE transaction journals, including IEEE Transactions on Evolutionary Computation, IEEE Transactions on Industrial Informatics, and IEEE Transactions on Cybernetics.
Kwong is actively engaged in knowledge exchange between academia and industry.  Kwong has a prolific research record. He has co-authored three research books, eight book chapters, and over 300 technical papers. According to Google Scholar, his works have been cited more than 25,000 times with an h-index of 70. He has been the distinguished lecturer of IEEE SMCS since 2018 and delivers two DL lectures yearly to promote the IEEE SMC Society and cutting-edge cybernetics technology. He also frequently delivers keynote speeches at IEEE-supported conferences. In 2014, he was elevated to IEEE Fellow for his contributions to optimization techniques in cybernetics and video coding. He is also a fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) in 2022.
Kwong’s involvement in the multiple facets of IEEE has been extensive and committed throughout the years. For IEEE Systems, Man and Cybernetics Society (SMCS), he serves as Hong Kong SMCS Chapter Chairman, Board Member, Conference Coordinator, Membership Coordinator and Member of the Long Range Planning and Finance Committee, Vice President of Conferences and Meetings, Vice President of Cybernetics. He led the IEEE SMC Hong Kong Chapter to win the Best Chapter Award in 2011 and was awarded the Outstanding Contribution Award for his contributions to SMC 2015. He was the President-Elect of the IEEE SMC Society in 2021. Currently, he serves as the Junior Past President of the IEEE SMC Society.




Prof. Jun Li, China University of Geosciences, Wuhan, China

IEEE Fellow


Prof. Jun Li (Fellow, IEEE) received the B.S. degree in geographic information systems from Hunan Normal University, Changsha, China, in 2004, the M.E. degree in remote sensing from Peking University, Beijing, China, in 2007, and the Ph.D. degree in electrical engineering from the Instituto de Telecomunicações, Instituto Superior Técnico (IST), Universidade Técnica de Lisboa, Lisbon, Portugal, in 2011. Currently, she is a Full Professor with Geoscience Uniersity of China (Wuhan), Wuhan, China. She is serving as the Editor in Chief for the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (2021-).





Prof. Jinchang Ren, Robert Gordon University, U.K


Jinchang Ren received his B. Eng. degree in computer software, M.Eng. in image processing, and D. Eng. in computer vision, all from Northwestern Polytechnical University, Xi’an, China. He was also awarded a Ph.D. degree in Electronic Imaging and Media Communication from the University of Bradford, Bradford, U.K.
Currently he is a full Professor of Computing Sciences, Transparent Ocean Lead, and Director of the Hyperspectral Imaging Lab at the National Subsea Centre (NSC), Robert Gordon University, Aberdeen, U.K.
Dr. Ren is a Senior Member of IEEE. His research interests focus mainly on hyperspectral imaging, image processing, computer vision, big data analytics and machine learning. He has published 380+ peer reviewed journal/conferences articles, and acts as an Associate Editor for several international journals including IEEE TGRS and J. of the Franklin Institute et al. He has also chaired and co-chaired a number of conferences and workshops. His students have received many awards, including the Best PhD thesis from IET Image and Vision Section and various conferences/workshops.

Speech Title: Advances in Underwater Optical and Sonar Image Enhancement and Quality Assessment

Abstract: In underwater environments, imaging devices with optic sensors face numerous challenges including water turbidity, light attenuation, scattering, and the presence of particles, which collectively degrade image quality, reduce contrast, and distort colours. To mitigate these issues, SOund and NAvigation Ranging (SONAR) sensors are often employed to capture information using sound pulses reflected from the scene. This imaging technique serves as a valuable complement in scenarios with poor lighting conditions. However, sonar images also exhibit limitations such as lower resolution, susceptibility to environmental factors like salinity and temperature, vulnerability to underwater currents and noises from marine life, and the presence of shadow zones, complicating the differentiation between original objects and their associated shadows.
Despite these limitations, the fusion of these two modalities can yield highly informative results. Consequently, researchers have concentrated on developing quality assessment techniques to ensure the acquisition of high-quality data, supplemented by enhancement methods to further refine data quality. Enhancements encompass haze removal, contrast and resolution enhancement, and enhancement of colour distribution in optical images. Additionally, for sonar images, emphasis is placed on noise reduction and shadow removal to improve overall clarity and interpretability.
In this talk, we will discuss the devised quality assessment techniques for both image types utilizing machine learning algorithms trained to correlate images, mapped within a predetermined feature space, with corresponding quality scores. Our experimental findings have demonstrated the effectiveness of these methods, exhibiting strong correlation with human evaluations. Furthermore, to facilitate enhancement, we have introduced innovative deep neural network architectures enriched with attention-driven inception modules and autoencoders. Through experimentation, we have showcased the efficacy of our developed networks in enhancing image quality, mitigating noise, and demonstrating robust generalization capabilities across publicly available datasets. Finally, applications of the developed techniques to tackle real challenges in offshore energy, subsea operations, military and environmental sectors are demonstrated.