NCKUEE Faculty Data
Chinese Version
Associate Professor Chia-Hsiang Lin
Address
EE Building 9F R92907
Email
TEL
+886-6-2757575 ext.62335
Lab Weblink
Intelligent Hyperspectral Computing Lab
(R92931/ext.62400-1666)
Background
Educations
B.S. in Electrical Engineering, National Tsing Hua University (2010)
Ph.D. in Communications Engineering, National Tsing Hua University (2016)
Experiences
2022-present, Associate Professor, Miin Wu School of Computing, National Cheng Kung University
2022-present, Associate Professor, Department of Electrical Engineering, National Cheng Kung University
2023-present, Director, Division of International Relations, Office of International Affairs, National Cheng Kung University
2022-2023, Director, Division of Algorithm and Optimization, Center for Data Science, National Cheng Kung University
2019, Visiting Professor, University of Lisbon, Portugal
2019-2021, Assistant Professor, Miin Wu School of Computing, National Cheng Kung University
2019-2021, Assistant Professor, Department of Electrical Engineering, National Cheng Kung University
2018, Assistant Professor, Center for Space and Remote Sensing Research, National Central University
2017-2018, Postdoctoral Researcher, University of Lisbon, Portugal
2017, Postdoctoral Researcher, The Chinese University of Hong Kong, HK
2015-2016, Research Assistant, Virginia Tech, VA, USA
2014, Research Assistant, The Chinese University of Hong Kong, HK
Specialities
  • blind signal processing / unsupervised machine learning
  • hyperspectral image processing
  • quantum image processing
  • fast algorithm / big data optimization theory
  • deep learning
  • convex optimization
  • 5G/6G wireless communications
  • satellite remote sensing
  • bio-informatics / biomedical imaging
Publication
Journal
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  1. P.-W. Tang, Chia-Hsiang Lin, and Y.-R. Liu,“Transformer-driven inverse problem transform for fast blind hyperspectral image dehazing,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
  2. Chia-Hsiang Lin, T.-H. Lin, and J. Chanussot, “Quantum information-empowered graph neural network for hyperspectral change detection,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  3. J.-T. Lin, and Chia-Hsiang Lin, “SuperRPCA: A collaborative superpixel representation prior-aided RPCA for hyperspectral anomaly detection,” IEEE Transactions on Geoscience and Remote Sensing, 2023.
  4. S.-S. Young, Chia-Hsiang Lin, and Z.-C. Leng, “Unsupervised abundance matrix reconstruction transformer guided fractional attention mechanism for hyperspectral anomaly detection,” IEEE Transactions on Neural Networks and Learning Systems, 2023.
  5. Chia-Hsiang Lin , C.-Y. Hsieh, and J.-T. Lin, “CODE-IF: A convex/deep image fusion algorithm for efficient hyperspectral super-resolution,” IEEE Transactions on Geoscience and Remote Sensing, 2024.
  6. Chia-Hsiang Lin, and S.-S. Young, “Signal subspace identification for incomplete hyperspectral image with applications to various inverse problems,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2024.
  7. Chia-Hsiang Lin, C.-C. Hsu, S.-S. Young, C.-Y. Hsieh, and S.-C. Tai, “QRCODE: Quasi-Residual Convex Deep Network for Fusing Misaligned Hyperspectral and Multispectral Images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 62, pp. 1-15, 2024.
  8. Chia-Hsiang Lin, S.-H. Huang, T.-H. Lin, and P.-C. Wu, “Metasurface-empowered snapshot hyperspectral imaging with convex/deep (CODE) small-data learning theory,” accepted by Nature Communications, 2023.
  9. Chia-Hsiang Lin, M.-C. Chu, and P.-W. Tang, “CODE-MM: Convex deep mangrove mapping algorithm based on optical satellite images,” accepted by IEEE Transactions on Geoscience and Remote Sensing, 2023.
  10. Chia-Hsiang Lin, and T.-H. Lin, "Hyperspectral change detection using semi-supervised graph neural network and convex deep learning," accepted by IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1-18, 2023.
  11. Chia-Hsiang Lin, and Y.-Y. Chen, “HyperQUEEN: Hyperspectral quantum deep network for image restoration,” IEEE Transactions on Geoscience and Remote Sensing , vol. 61, pp. 1-20, 2023.
  12. Chia-Hsiang Lin, Y. Liu, C.-Y. Chi, C.-C. Hsu, H. Ren, and T. Q. S. Quek, “Hyperspectral tensor completion using low-rank modeling and convex functional analysis ,” accepted by IEEE Transactions on Neural Networks and Learning Systems, 2022.
  13. P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “Optimization-based hyperspectral spatiotemporal super-resolution,”IEEE Access, pp. 37477-37494, 2022.
  14. L. Chen, C.-T. Wu, Chia-Hsiang Lin, R. Dai, C. Liu, R. Clarke, G. Yu, J. E. Van Eyk, D. M. Herrington, and Y. Wang, “swCAM: estimation of subtype-specific expressions in individual samples with unsupervised sample-wise deconvolution,”Bioinformatics, vol. 38, no. 5, pp. 1403-1410, 2022.
  15. Chia-Hsiang Lin, Y.-C. Lin, and P.-W. Tang, “ADMM-ADAM: A new inverse imaging framework blending the advantages of convex optimization and deep learning,”IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 1, pp. 1-16, 2022.
  16. C.-H. Lee, R. Chang, S.-M. Cheng, Chia-Hsiang Lin, and C.-H. Hsiao, “Joint beamforming and power allocation for M2M/H2H co-existence in green dynamic TDD networks: Low-complexity optimal designs,” IEEE Internet of Things Journal, vol. 9, no. 6, pp. 4799-4815, 2022.
  17. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, H.-C. Wu, H.-T. Kuo, C.-F. Lin, P. Chen, and P.-C. Wu, “Automatic inverse design of high-performance beam-steering metasurfaces via genetic-type tree optimization,” Nano Letters, vol. 21, no. 12, pp. 4981-4989, Jun. 2021.
  18. Chia-Hsiang Lin, and T.-H. Lin, “All-addition hyperspectral compressed sensing for metasurface-driven miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 1, pp. 1-15, 2022.
  19. C.-C. Hsu, Chia-Hsiang Lin, C.-H. Kao, and Y.-C. Lin, “DCSN: Deep compressed sensing network for efficient hyperspectral data transmission of miniaturized satellite,” IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 9, pp. 7773-7789, Sep. 2021.
  20. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Non-negative blind source separation for ill-conditioned mixtures via John ellipsoid,” IEEE Transactions on Neural Networks and Learning Systems, vol. 32, no. 5, pp. 2209-2223, May 2021.
  21. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “An explicit and scene-adapted definition of convex self-similarity prior with application to unsupervised Sentinel-2 superresolution,” IEEE Transactions on Geoscience and Remote Sensing, vol. 58, no. 5, pp. 3352-3365, May 2020.
  22. L. Zhuang, Chia-Hsiang Lin, M. A. T. Figueiredo, and J. M. Bioucas-Dias, “Regularization parameter selection in minimum volume hyperspectral unmixing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 57, no. 12, pp. 9858-9877, Dec. 2019.
  23. Y.-R. Syu, Chia-Hsiang Lin, and C.-Y. Chi, “An outlier-insensitive unmixing algorithm with spatially varying hyperspectral signatures,” IEEE Access, vol. 7, pp.15086-15101, Jan. 2019.
  24. Chia-Hsiang Lin, C.-Y. Chi, L. Chen, D. J. Miller, and Y.Wang, “Detection of sources in non-negative blind source separation by minimum description length criterion,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 9, pp. 4022-4037, Sep. 2018.
  25. Chia-Hsiang Lin, R. Wu, W.-K. Ma, C.-Y. Chi, and Y. Wang, “Maximum volume inscribed ellipsoid: A new simplex-structured matrix factorization framework via facet enumeration and convex optimization,” SIAM Journal on Imaging Sciences, vol. 11, no. 2, pp. 1651-1679, Jun. 2018.
  26. Chia-Hsiang Lin, F. Ma, C.-Y. Chi, and C.-H. Hsieh, “A convex optimization based coupled non-negative matrix factorization algorithm for hyperspectral and multispectral data fusion,” IEEE Transactions on Geoscience and Remote Sensing, vol. 56, no. 3, pp. 1652-1667, Mar. 2018.
  27. G. Xu, Chia-Hsiang Lin, W. Ma, S. Chen, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” IEEE Access, vol. 5, pp. 13601-13616, Mar. 2017.
  28. Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based minimum-volume enclosing simplex algorithm for blind hyperspectral unmixing,” IEEE Transactions on Signal Processing, vol. 64, no. 8, pp. 1946-1961, Apr. 2016.
  29. A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, F.-S. Yang, C.-Y. Chi, and Y. Wang, “Convex optimization-based compartmental pharmacokinetic analysis for prostate tumor characterization using DCE-MRI,” IEEE Transactions on Biomedical Engineering, vol. 63, no. 4, pp. 707-720, Apr. 2016.
  30. Chia-Hsiang Lin, W.-K. Ma, W.-C. Li, C.-Y. Chi, and A. Ambikapathi, “Identifiability of the simplex volume minimization criterion for blind hyperspectral unmixing: The no pure-pixel case,” IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 10, pp. 5530-5546, Oct. 2015.
Conference
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  1. Chia-Hsiang Lin, S.-S. Young, C. Liu, L.-Y. Chang, and T.-Y. Liao, “Image resolution enhancing of Sentinel-2 red edge bands via Pléiades-1 multispectral data and fast convex deep learning,” SPIE Asia-Pacific Remote Sensing Symposium, Kaohsiung, Taiwan, Dec. 2-4, 2024.
  2. S.-S. Young, Chia-Hsiang Lin, J.-Y. Chen, and J.-K. Huang, “HyperQUEEN-CD: Quantum neural network for unsupervised hyperspectral change detection,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Hualien, Taiwan, Aug. 18-20, 2024.
  3. G.-J. Wei, Chia-Hsiang Lin, and S.-M. Hsu, “A channel-wise quantum attention mechanism for RGB and hyperspectral image super-resolution,” IPPR Conference on Computer Vision, Graphics, and Image Processing, Hualien, Taiwan, Aug. 18-20, 2024.
  4. (Invited Paper) S.-M. Hsu, T.-H. Lin, and Chia-Hsiang Lin, “HyperQUEEN-MF: Hyperspectral quantum deep network with multi-scale feature fusion for quantum image super-resolution,” accepted by IEEE SAM, Corvallis, OR, USA, July 8-11, 2024.
  5. Chia-Hsiang Lin, C.-Y. Kuo, and S.-S. Young, “Quantum adversarial learning for hyperspectral remote sensing,” accepted by IEEE IGARSS, Athens, Greece, July 7-12, 2024
  6. Chia-Hsiang Lin, S.-S. Young, L.-Y. Chang, and Cynthia S.J. Liu, “Synthesis of high-resolution FORMOSAT-8 satellite image using fast convex deep learning algorithm,” accepted by IEEE IGARSS, Athens, Greece, July 7-12, 2024
  7. S.-S. Young, *Chia-Hsiang Lin, and J.-T. Lin, “CiDAR-Former: Cosine-weighting deep abundance reconstruction transformer for fast unsupervised hyperspectral anomaly detection,” accepted by IEEE WHISPERS, Athens, Greece, Oct. 31-Nov. 2, 2023.
  8. T.-H. Lin, and *Chia-Hsiang Lin, and S.-S. Young, “GNN-based small-data learning with area-control mechanism for hyperspectral satellite change detection,” accepted by Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Taipei, Taiwan, Oct. 31-Nov. 3, 2023.
  9. Chia-Hsiang Lin, and Y.-Y. Chen, “Quantum deep hyperspectral satellite remote sensing,” IEEE IGARSS, Pasadena, California, July 16-21, 2023.
  10. Chia-Hsiang Lin, M.-C. Chu, and H.-J. Chu, “High-dimensional multiresolution satellite image classification: An approach blending the advantages of convex optimization and deep learning,”IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  11. Chia-Hsiang Lin, T.-H. Lin, T.-H. Lin, and T.-H. Lin, “Fast reconstruction of hyperspectral image from its RGB counterpart using ADMM-Adam theory,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  12. Y. Liu, Chia-Hsiang Lin, and Y.-C. Kuo, “Low-rank representation with morphological-attribute-filter based regularization for hyperspectral anomaly detection,”IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  13. P.-W. Tang, and Chia-Hsiang Lin, “Hyperspectral dehazing using ADMM-Adam theory,”IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  14. P.-C. Chuan, J.-T. Lin, Chia-Hsiang Lin, P.-W. Tang, and Y. Liu, “A fast multidimensional data fusion algorithm for hyperspectral spatiotemporal super-resolution,” IEEE WHISPERS, Rome, Italy, Sep. 13-16, 2022.
  15. T.-H. Lin, and Chia-Hsiang Lin, “Single hyperspectral image super-resolution using ADMM-Adam theory,” IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  16. J.-T. Lin, and Chia-Hsiang Lin, “Real-time hyperspectral anomaly detection using collaborative superpixel representation with boundary refinement,”IEEE IGARSS, Kuala Lumpur, Malaysia, July 17-22, 2022.
  17. C.-H. Yu, Z.-C. Leng, Y. Liu, J.-Y. Huang, Chia-Hsiang Lin, and T.-Y. Tu, “A total solutioning workflow for sample processing and precise nuclei quantification in 3D tumor spheroids using unsupervised algorithm,”World Congress of Biomechanics, Taipei, Taiwan, Jul. 10-14, 2022.
  18. A. Hassanfiroozi, Chia-Hsiang Lin, J.-T. Lin, and P.-C.Wu, “High-performance metasurfaces for wavefront engineering,”Materials Research Society Fall Meeting and Exhibit, Boston, MA, USA, Nov. 28 - Dec. 3, 2021.
  19. C.-H. Kao, Chia-Hsiang Lin, S.-W. Jian, and P.-Y. Lin, “Solving hyperspectral single image super-resolution via fusion-based inverse problem transform,” The 34th IPPR Conference on Computer Vision, Graphics, and Image Processing, Taipei, Taiwan, Aug. 22-24, 2021. (“Outstanding Paper Award”)
  20. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, Y.-C. Cheng, A. Hassanfiroozi, H.-C. Wu, H.-T. Kuo, and P.-C. Wu, “Toward high-performance plasmonic metasurfaces: From forward to inverse design approach,” SPIE Optics and Photonics, San Diego, CA, USA, Aug. 1-5, 2021.
  21. Chia-Hsiang Lin, C.-Y. Sie, P.-Y. Lin, and J.-T. Lin, “Fast unsupervised spatiotemporal super-resolution for multispectral satellite imaging using plug-and-play machinery strategy,” IEEE IGARSS, Brussels, Belgium, Jul. 11-16, 2021.
  22. Chia-Hsiang Lin, Y.-C. Lin, P.-W. Tang, and M.-C. Chu, “Deep hyperspectral tensor completion just using small data,” IEEE IGARSS, Brussels, Belgium, July 11-16, 2021.
  23. Chia-Hsiang Lin, and P.-W. Tang, “Inverse problem transform: Solving hyperspec- tral inpainting via deterministic compressed sensing,”IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  24. Chia-Hsiang Lin, and Y. Liu, “Blind hyperspectral inpainting via John ellipsoid,” IEEE WHISPERS, Amsterdam, Netherlands, Mar. 24-26, 2021.
  25. Chia-Hsiang Lin, Y.-S. Chen, J.-T. Lin, and P.-C. Wu, “Inverse design of non- periodical metasurfaces via high-performance automatic optimization,” in Proc. Op- tics & Photonics Taiwan International Conference (OPTIC), Taipei, Taiwan, Dec. 3-5, 2020.
  26. C.-C. Hsu, W.-H. Zheng, H.-T. Yang, Chia-Hsiang Lin, and C.-H. Kao, “Rethinking relation between model stacking and recurrent neural networks for social media prediction,” in Proc. ACM Multimedia (MM), Seattle, WA, USA, Oct. 12-16, 2020. (“Invited Paper”) (“Top Performance Award”)
  27. Y.-C. Hung*, Chia-Hsiang Lin*, F.-Y. Wang, and S.-H. Yang, “Penetrating tera- hertz hyperspectral unmixing via Lo ̈wner-John ellipsoid: An unsupervised algorithm,” in Proc. IRMMW-THz, Buffalo, NY, USA, Sep. 13-18, 2020. (*Contributed Equally)
  28. C.-C. Hsu, Y.-C. Lin, C.-H. Kao, and Chia-Hsiang Lin, “Deep joint compression and super-resolution low-rank network for fast hyperspectral data transmission,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”)
  29. T.-H. Lin, Chia-Hsiang Lin, Y. Liu, and C.-H. Kao, “A simple spatial-spectral proximal compression method for high-dimensional imagery with proximal computing based blind reconstruction,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”)
  30. C.-Y. Sie, Chia-Hsiang Lin, P.-W. Tang, and Y.-C. Lin, “Solving the algebraic hyperspectral inpainting problem: A fast hyperplane geometry based approach,” The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020. (“Invited Paper”) (“Outstanding Paper Award”)
  31. Chia-Hsiang Lin, J. M. Bioucas-Dias, T.-H. Lin, Y.-C. Lin, and C.-H. Kao, “A new hyperspectral compressed sensing method for efficient satellite communications,” in Proc. IEEE SAM, Hangzhou, China, June 8-11, 2020. (“Invited Paper”)
  32. W.-C. Zheng, K.-H. Tseng, and Chia-Hsiang Lin, “Unsupervised change detection using convex relaxation and dynamic threshold selection in remotely sensed images,” American Geophysical Union (AGU) Fall Meeting, San Francisco, CA, USA, Dec. 9-13, 2019.
  33. C.-C. Hsu, and Chia-Hsiang Lin, “Dual reconstruction with densely connected residual network for single image super-resolution,” in Proc. IEEE ICCV, Seoul, Korea, Oct.27 - Nov. 2, 2019. (“Invited Paper”)
  34. C.-H. Wang, K.-H. Tseng, and Chia-Hsiang Lin, “Waterline detection using fusion based super-resolution of multispectral satellite image with self-similarity,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.
  35. T.-Y. Lin, H. Ren, and Chia-Hsiang Lin, “Bathymetry estimation via convex geometry in multispectral satellite imagery: A case study in Dongsha Atoll,” The 38th Conference on Surveying and Geoinformatics, Taoyuan, Taiwan, Aug. 29-30, 2019.
  36. W.-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, T.-H. Lin, C.-H. Wang, and C.-Y. Chi, “Unsupervised change detection in multitemporal multispectral satellite images: A convex relaxation approach,” in Proc. IEEE IGARSS, Yokohama, Japan, Jul. 28 - Aug. 2, 2019.
  37. C.-H. Wang, Chia-Hsiang Lin, J. M. Bioucas-Dias, W.-C. Zheng, and K.-H. Tseng, “Panchromatic sharpening of multispectral satellite imagery via an explicitly defined convex self-similarity regularization,” in Proc. IEEE IGARSS, Yokohama, Japan, Jul. 28 - Aug. 2, 2019. (“Interactive Session Prize Paper Award”)
  38. W-C. Zheng, Chia-Hsiang Lin, K.-H. Tseng, C.-Y. Huang, and T.-H. Lin, “Criterion design and large-scale optimization algorithm for blind change detection in multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.
  39. C.-H. Wang, Chia-Hsiang Lin, and K.-H. Tseng, “Patch similarity guided super-resolution algorithm for fusing panchromatic and multispectral images,” International Symposium on Remote Sensing, Taipei, Taiwan, Apr. 17-19, 2019.
  40. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Linear spectral unmixing via matrix factorization: Identifiability criteria for sparse abundances,” in Proc. IEEE IGARSS, Valencia, Spain, Jul. 23-27, 2018.
  41. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “New theory for unmixing ill-conditioned hyperspectral mixtures,” in Proc. IEEE SAM, Sheffield, UK, Jul. 8-11, 2018. (“Invited Paper”)
  42. Chia-Hsiang Lin, and J. M. Bioucas-Dias, “Provably and robust blind source separation of ill-conditioned hyperspectral mixtures,” in Proc. IEEE SSP, Freiburg, Germany, Jun. 10-13, 2018.
  43. G. Xu, Chia-Hsiang Lin, W. Ma, and C.-Y. Chi, “Outage constrained robust hybrid coordinated beamforming for massive MIMO enabled heterogeneous cellular networks,” in Proc. IEEE ICC, Paris, France, May 21-25, 2017.
  44. W.-K. Ma, Chia-Hsiang Lin, W.-C. Li, and C.-Y. Chi, “When can the minimum volume enclosing simplex identify the endmembers correctly when there is no pure pixel?,” in Proc. IEEE WHISPERS, Tokyo, Japan, Jun. 2-5, 2015.
  45. Chia-Hsiang Lin, C.-Y. Chi, Y.-H. Wang, and T.-H. Chan, “A fast hyperplane-based MVES algorithm for hyperspectral unmixing,” in Proc. IEEE ICASSP, Brisbane, Australia, Apr. 19-24, 2015.
  46. A. Ambikapathi, T.-H. Chan, Chia-Hsiang Lin, and C.-Y. Chi, “Convex geometry based outlier-insensitive estimation of number of endmembers in hyperspectral images,” in Proc. IEEE WHISPERS, Gainesville, Florida, USA, Jun. 25-28, 2013. (“Invited Paper”)
  47. Chia-Hsiang Lin, A. Ambikapathi, W.-C. Li, and C.-Y. Chi, “On the endmember identifiability of Craig’s criterion for hyperspectral unmixing: A statistical analysis for three-source case,” in Proc. IEEE ICASSP, Vancouver, Canada, May 26-31, 2013.
Patent
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Others
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  1. C.-Y. Chi, W.-C. Li, and Chia-Hsiang Lin, Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press, Boca Raton, FL, Feb. 2017. (Available in CRC Press; also available in Taiwan SCI-TECH.)
  2. Chinese version (信号处理与通中的凸优化: 从基础到应用) translated by Chen Xiang (陈翔) and Shen Chao (沈超) and published by 电子工业出版社, Dec. 2020.
Projects
  1. HyperQUEEN: Hyperspectral Quantum Deep Network for Space Remote, National Science and Technology Council, 2023 to 2027
  2. Advanced Blind Source Separation and Hyperspectral Super-resolution Imaging via Convex Geometry and Big Data Optimization, Ministry of Science and Technology (Einstein Program), from 2018 to 2023
  3. Mathematical Theory and Metagrating Design for Advanced Satellite Imaging, Ministry of Education, 2019 to 2022
  4. Development of Dynamic Electrical Resistivity Tomography Spectrum: Integration of Numerical Solutions and Disaster Prevention Applications (Co-PI), Ministry of Education, 2019
Classes
2019 Spring
2019 Fall
2020 Spring
2020 Fall
2021 Spring
2021 Fall
2022 Spring
2022 Fall
2023 Spring
2023 Fall
Students
Current Academic Year Lab Members
Ph.D.
Tzu-Hsuan Lin
Po-Wei Tang
Jhao-Ting Lin
Zi-Chao Leng
Si-Sheng Young
Shih-Min Hsu
Master
Guang-Jie Wei
Yi-Xuan Zhong
Chen-Yu Kuo
Wei-Heng Li
Jhih-Yan Chen
Cheng-Wei Fan
Yung-Sheng Liang
Wei-Chun Lin
Jian-Kai Huang
Bo-Yu Lin
Chen-Yu Liu
Jing-Yun Liang
Undergraduate
Ting-Jyun Yang
Shang-Jyun Shih
Bing-Rong Yang
Yu-Rong Jhang
Chien-Hao Wang
Hsien-Chin Liao
Graduates of all Previous Years
Master
109
Chi-Hung Kao   Cheng-Yu Sie   Yen-Cheng Lin
110
Man-Chun Chu   Shao-Wei Jian
111
Ting-Hsuan Lin   Gheng-Ying Hsieh   Kuan-Huang Yu   Yo-Yu Lai
Bachelor
109
Yi-Xun Li   Zi-Chao Leng
110
Hsiao-Ching Huang   Wan-Hsuan Lin   Pai-Chuan Chang   You-Yao Chen
111
Yen-Ting Ho   Jong-Zing Sui   Che-Yu Wu
Honors
  1. 2024 GENESYS Logic Paper Excellence Award
  2. Excellent EMI Teaching Innovation Award of University Social Responsibility (USR) from NCKU
  3. 113th National Salvation Corps Youth Medal Winner,2024.
  4. Emerging Young Scholar Award from National Science and Technology Council (2030 Cross-Generation Young Scholars Program)
  5. Teaching Excellence Award, National Cheng Kung University, 2023.
  6. Excellent Mentor Award, National Cheng Kung University, 2023.
  7. Outstanding Poster Exhibition Award, Communications Engineering Program, National Science and Technology Council, 2023.
  8. Outstanding Youth Electrical Engineer Award, The Chinese Institute of Electrical Engineering, 2022.
  9. FutureTech Award, National Science and Technology Council, 2022.
  10. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2022.
  11. Outstanding Poster Exhibition Award, Communications Engineering Program, Ministry of Science and Technology, 2022.
  12. Best Young Professional Member Award, IEEE Tainan Section, 2021.
  13. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2021.
  14. Excellent Research Project Award, Communications Engineering Program, Ministry of Science and Technology, 2021.
  15. Top Performance Award, Social Media Prediction Challenge, ACM Multimedia, 2020.
  16. Outstanding Paper Award from IPPR Conference on Computer Vision, Graphics, and Image Processing (CVGIP), 2020.
  17. Symposium Interactive Session Prize Paper Award (IGARSS'19) from IEEE Geoscience & Remote Sensing Society, 2020.
  18. 3rd Place (Objective) and 5th Place (Subjective), AIM Real World Super-Resolution Challenge, IEEE International Conference on Computer Vision (ICCV), 2019.
  19. Einstein Grant Award from Ministry of Science and Technology, from 2018 to 2023.
  20. Best Doctoral Dissertation Award from IEEE Geoscience & Remote Sensing Society, 2016.
  21. Outstanding Dissertation Award, Chinese Image Processing and Pattern Recognition (IPPR) Society, 2016.
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