Yi-Chun Hung

Computational Imaging and Computer Vision


Mr. Yi-Chun Hung received his M.S. degree in electrical and computer engineering from University of California, Los Angeles. He has conducted multiple projects related to computational imaging and computer vision. The outcomes of his research works were published into several international journals and proceeding conference papers. He has several patents in Taiwan and the US. He has also received several international awards and scholarships including J. Yang scholarship (2020), National Science Council Research Scholarship, Taiwan (2018) and IEEE Arctic Challenge Sponsorship (2018, only 6 winners over the world). Additionally, he is an action-oriented educator to inspire his juniors to realize creative ideas. To this end, he launched a semester-long cultivating program in 2017 to encourage juniors for original and innovative projects. The program benefited over 50 juniors and has been officially supported by the department. The program has been run by juniors for over 3 years.


All-Optical Diffractive Deep Neural Network

Sep 2020 ~ Apr 2022

  • Implemented and trained a broadband diffractive neural network that delivers superior performance on output quality and power efficiency compared to some of our previously demonstrated results.
  • Proposed new loss function to accurately control the output spectral profile of a broadband diffractive network.
  • Proposed an innovative modular design for mechanically assembling parts of diffractive networks with high precision and reconfigurability.

Terahertz Deep Learning Computed Tomography

Oct 2018 - Present

  • Designed the first supervised deep learning model based on terahertz time domain spectroscopy (THz-TDS) system to achieve the image resolution closed to diffraction limit.
  • Established the precise THz-TDS dataset, containing over 600 gigabytes raw signal data.
  • Utilized distributed computing system with automated hyperparameter tunning tool to improve the training efficiency.

Terahertz Compressed Sensing Imaging

May 2019 - Present

  • Built the terahertz compressed sensing optical system by a 532nm laser on a spatial light modulator.
  • Implemented optimization based reconstruction algorithms, such as Hadamard matrix with Lasso.
  • Collaborating with the professor in polymer material design to improve the modulation efficiency.

Terahertz Hyperspectral Imaging

Sep 2019 - Oct 2021

  • Formulated a terahertz data model for blind spectral unmixing algorithms.
  • Designed the precise experiments to validate the assumptions in the formulated model.
  • Collaborating with the professor in the optimization field to design the imaging reconstruction algorithm.

Seleted Interdisciplinary Projects

Material Parameters Extraction Among Terahertz Region

Sep 2019 - Present
  • Implementing the optimization based algorithm to extract the refractive index and absortion coefficient of the materials based on the Terahertz time domain system.

Learning-Based Autopiloted Cart

Dec 2017
  • Implemented lane following carts with object detection to avoid obstacles on Udoo Neo and K64F.

Interactive RPG Mobile Game iOS Application

Jan 2017 - Aug 2017
  • Implemented an IOS application for over 100 freshman welcome camp, including utilizing google map API, restful API, and data synchronizing with the self-build server.

Work Experience

Research Assistant, Yang Research Group

Dept. EE, NTHU | Sep 2019 - Jan 2021

  • Leading interdisciplinary projects relative to optics, numerical modeling, and optimization.
  • Collaborating with professors from different fields, such as digital IC design, optimization, and polymer material.

Teaching Assistant, Embedded System Laboratory

Dept. EE, NTHU | Dec 2017 - July 2018

  • Organized the teaching material for image recongnition based on deep learning onto embedded platform.
  • Implemented the lane following control system for the embedded cart based on image processing.

Research Assistant, Intelligent Hyperspectral Computing

Dept. EE, NCKU | Apr 2022 - Oct 2022

  • Proposed a hyhrid quantum-classical network for image restoration.
  • Developed a model-based hyperspectral video motion deblur method.


[J1] Y.-C. Hung, T.-H. Chao, P. Yu, S.-H. Yang, “Terahertz Deep Learning Computed Tomography Framework,” Opt. Express 30, 22523-22537 (2022)

[J2] J. Li, Y.-C. Hung, O. Kulce, et al., “Polarization multiplexed diffractive computing: all-optical implementation of a group of linear transformations through a polarization-encoded diffractive network.” Light Sci Appl 11, 153 (2022).

[J3] W.-T. Su, Y.-C. Hung, P.-J. Yu, et al., “Physics-guided Terahertz Computational Imaging.” IEEE Signal processing magazine, 2022 (Accepted).

[J4] W.-C. Wang, Y.-C. Hung, Y.-H. Du, S.-H. Yang, Y.-H. Huang, “FPGA-Based Tensor Compressive Sensing Reconstruction Processor for Terahertz Single-Pixel Imaging Systems.” IEEE Open Journal of Circuits and Systems, vol. 3, pp. 336-350, 2022.

[J5] Y.-C. Hungx, C.-H. Linx, F.-Y. Wang, S.-H. Yang, “Terahertz Hyperspectral Penetrating-type Ellipsoidal Reconstruction” Preprint on arXiv - http://arxiv.org/abs/2109.05425

[C1] Y.-C. Hung, S.-H. Yang, “Terahertz Deep Learning Computed Tomography”, Proc. International Conference on Infrared, Millimeter, and Terahertz Waves, Paris, France (2019) – Oral Presentation

[C2] Y.-C. Hung, S.-H. Yang, “Kernel Size Characterization for Deep Learning Terahertz Tomography”, Proc. International Conference on Infrared, Millimeter, and Terahertz Waves, Paris, France (2019) – Poster Presentation

[C3] B.-Y. Wu, Y.-C. Hung, S.-H. Yang, “Plasmonic-Enhanced Terahertz Tomography System”, Proc. International Conference on Computational Electromagnetics, Singapore (2020)

[C4] K.T.Z Htun, B.-Y. Wu, Y.-C. Hung, S.-H. Yang, “Terahertz LASSO Compressed Sensing Tomography System”, Proc. SPIE Photonic Europe, Strasbourg, France (2020)

[C5] Y.-C. Hung, C.-H. Lin, F.-Y. Wang, S.-H. Yang, “Penetrating Terahertz Hyperspectral Unmixing via Löwner-John Ellipsoid (THz HU-LJE): An Unsupervised Algorithm”, Proc.International Conference on Infrared, Millimeter, and Terahertz Waves, Buffalo, NY USA (2020)

[C6] Z.-H. Tu, Y.-C. Hung, S.-H. Yang, “Terahertz Deep Learning Super Resolution Imaging Training on Sinogram”, Proc.International Conference on Infrared, Millimeter, and Terahertz Waves, Buffalo, NY USA (2020)

[C7] (Invited Paper) Y.-C. Hung, S.-H. Yang, “Terahertz 3D Computed Tomography Processed by Deep Learning”, The 33rd IPPR Conference on Computer Vision, Graphics, and Image Processing, Hsinchu, Taiwan, Aug. 16-18, 2020

[C8] W.-T. Su, Y.-C. Hung, P.-J. Yu, C.-W. Lin, S.-H. Yang, “Seeing through a Black Box: Toward High-Quality Terahertz Imaging via Subspace-and-Attention Guided Restoration”, European Conference on Computer Vision. Springer, Tel Aviv, 2022