Keng Liang Ou

Work place: College of Oral Medicine, Taipei Medical, University, Taiwan.



Research Interests: Medicine, Computational Biology, Bioinformatics


Professor Keng-Liang Ou obtained Ph.D. degree from Graduate Institute of Mechanical Engineering, National Chiao Tung University, Taiwan. He came to Taipei Medical University, Taiwan to pursue cutting edge research for biomaterials and currently holds the position of elected Dean of College of Oral Medicine. He is also in charge of Graduate Institute of Biomedical Materials and Tissue Engineering, Research Center for Biomedical Implants and Microsurgery Devices and Research Center for Biomedical Devices and Prototyping Production. In addition to institutional appointment, Prof. Ou also serves as the President of Institute of Plasma Engineering Taiwan, the leader of the Taiwan society for metal heat treatment and the Head of Taiwan Oral Biomedical Engineering Association, Professor Ou devotes energy to novel research in the fields of biomaterials, bioengineering, biosensing and bioimaging, as well as translation medicine. In addition, he has extensive collaborations with industry and has played a major role in developing medical devices for health professionals worldwide. He is the leader and organizer for the biomedical product design, production, manufacturing, testing, legalization and marketing planning, with supports from team of scientists and researchers with different expertise. These efforts were well recognized by the community and Professore Ou was awarded with the 49th Ten Outstanding Young Persons of Taiwan at the year of 2011.

Author Articles
Simultaneous Image Fusion and Denoising based on Multi-Scale Transform and Sparse Representation

By Tahiatul Islam Sheikh Md. Rabiul Islam Xu Huang Keng Liang Ou

DOI:, Pub. Date: 8 Jun. 2017

Multi-scale transform (MST) and sparse representation (SR) techniques are used in an image representation model. Image fusion is used especially in medical, military and remote sensing areas for high resolution vision. In this paper an image fusion technique based on shearlet transformation and sparse representation is proposed to overcome the natural defects of both MST and SR based methods. The proposed method is also used in different transformations and SR for comparison purposes. This research also investigate denoising techniques with additive white Gaussian noise into source images and perform threshold for de-noised into the proposed method. The image quality assessments for the fused image are used for the performance of proposed method and compared with others. 

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