Page 81 - 2017 Brochure
P. 81
究員
黃文良 Wen-Liang Hwang
Research Fellow
Ph.D., Computer Science, New York University
Tel: +886-2-2788-3799 ext. 1609 Fax: +886-2-2782-4814
Email: whwang@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/whwang/
• Research Fellow, Institute of Information Science, Academia Sinica (2005-present)
• Associate Research Fellow, Institute of Information Science, Academia Sinica (1999-2005)
• Assistant Research Fellow, Institute of Information Science, Academia Sinica (1995-1999)
• Postdoctoral Researcher, Department of Mathematics, University of California, Irvine (1993-1994)
• Ph.D., Computer Science, New York University (1993)
• M.S., Electrical Engineering, Polytechnic Institute of New York (now New York University), (1988)
• B.S., Nuclear Engineering, National Tsing Hua University (1981)
Research Description and sparse representation: Dr. Hwang uses the frame theory to
bridge the analysis-based sparse model and the synthesis-based
Dr. Hwang’s research areas are signal and image processing, sparse model. He shows that the frame-analysis sparse problem
wavelet and time-frequency analysis, and mathematical analysis. can be regarded as a dual of the frame-synthesis problem and
In addition to co-authoring one book on time-frequency analysis, demonstrates the gap between the optimal values of the two
he has had several technique papers published in leading journals. problems. He also presents the conditions to zip the gap and uses
In 2001, he received a national award for distinguished junior the conditions in the design and implementation of the frame-
researchers in Taiwan. Dr. Hwang is listed on the ISI Highly Cited synthesis K-SVD and frame-analysis K-SVD learning methods. (3)
Researchers List. Multi-layer neural networks: Dr. Hwang is interested in theoretical
issues related to multi-layer neural networks, including optimization
Dr. Hwang’s recent interests are as follows. (1) Convex analysis methods in learning the networks. He also has collaborations
and adaptive operator: The adaptive operator approach can with biologists seeking to discover the similarity and dissimilarity
be regarded as an extension of the proximal method in convex between the networks in computational models, and in the retinex
analysis. Each variable in the optimization is associated with an and the olfactory systems. (4) Image processing: In addition, Dr.
operator that distinguishes the variable from others. The operators Hwang studies fundamental image processing problems, such as
and the variables are updated at each iteration for optimal solutions. de-noising, de-blurring, segmentation, and compression problems,
Dr. Hwang studies the convergence and other mathematical using state-of-the-art analysis methods.
properties of the approach and has successfully applied it in source
separation problems and the sparse representation problem, even
with non-convex priors. (2) Frame-based compressive sensing
Publications 8. Jinn Ho and Wen-Liang Hwang, “Wavelet Bayesian Network Image
Denosing”, IEEE Trans. on Image Processing, Vol. 22, Issue 4, April
1. Stephane Mallat and Wen-Liang Hwang, “Singularity Detection 2013.
and Processing with Wavelets”, IEEE Transactions on Information
Theory, vol. 38, no. 2, pp. 617-645, March 1992. 9. Guan-Ju Peng and Wen-Liang Hwang, “Reweighted and Adaptive
Morphology Separation”, SIAM J. Imaging Sci. 7- 4, 2014, pp. 2048-
2. Rene Carmona, Wen-Liang Hwang, and Bruno Torresani, “Practical 2077.
Time-Frequency Analysis”, Academic Press, 1998.
10. Wen-Liang Hwang, Keng-Shih Lu and Jinn Ho, “Constrained
3. Ming-Shing Su, Wen-Liang Hwang, and Kuo-Yourn Cheng, “Analysis Null Space Component Analysis for Semiblind Source Separation
on Multiresolution Mosaic Images”, IEEE Transactions on Image Problem”, IEEE Transactions on Neural Networks and Learning
Processing, vol. 13, no. 7, pp. 952-959, July 2004. Systems, To Appear.
4. Chun-Liang Tu, Wen-Liang Hwang, and Jinn Ho, “Analysis of
Singularities from Modulus Maxima of Complex Wavelets”, IEEE
Transactions on Information Theory, vol. 51, no. 3, pp. 1049-1062,
March 2005.
5. Silong Peng and Wen-Liang Hwang, ‘’Adaptive Signal Decomposition
based on Local Narrow Band Signals”, IEEE Trans. on Signal
Processing, vol. 56, pp. 2669-2676, July 2008.
6. Silong Peng and Wen-Liang Hwang, “Null Space Pursuit: An
Operator-based Approach to Adaptive Signal Separation”, IEEE
Trans. on Signal Processing, vol. 58, pp. 2475-2483, May 2010.
7. Xiyuan Hu, Silong Peng, and Wen-Liang Hwang, “EMD Revisited: A
New Understanding of the Envelope and Resolving the Mode-Mixing
Problem in AM-FM Signals”, IEEE Trans. on Signal Processing,
March 2012.
79
黃文良 Wen-Liang Hwang
Research Fellow
Ph.D., Computer Science, New York University
Tel: +886-2-2788-3799 ext. 1609 Fax: +886-2-2782-4814
Email: whwang@iis.sinica.edu.tw
http://www.iis.sinica.edu.tw/pages/whwang/
• Research Fellow, Institute of Information Science, Academia Sinica (2005-present)
• Associate Research Fellow, Institute of Information Science, Academia Sinica (1999-2005)
• Assistant Research Fellow, Institute of Information Science, Academia Sinica (1995-1999)
• Postdoctoral Researcher, Department of Mathematics, University of California, Irvine (1993-1994)
• Ph.D., Computer Science, New York University (1993)
• M.S., Electrical Engineering, Polytechnic Institute of New York (now New York University), (1988)
• B.S., Nuclear Engineering, National Tsing Hua University (1981)
Research Description and sparse representation: Dr. Hwang uses the frame theory to
bridge the analysis-based sparse model and the synthesis-based
Dr. Hwang’s research areas are signal and image processing, sparse model. He shows that the frame-analysis sparse problem
wavelet and time-frequency analysis, and mathematical analysis. can be regarded as a dual of the frame-synthesis problem and
In addition to co-authoring one book on time-frequency analysis, demonstrates the gap between the optimal values of the two
he has had several technique papers published in leading journals. problems. He also presents the conditions to zip the gap and uses
In 2001, he received a national award for distinguished junior the conditions in the design and implementation of the frame-
researchers in Taiwan. Dr. Hwang is listed on the ISI Highly Cited synthesis K-SVD and frame-analysis K-SVD learning methods. (3)
Researchers List. Multi-layer neural networks: Dr. Hwang is interested in theoretical
issues related to multi-layer neural networks, including optimization
Dr. Hwang’s recent interests are as follows. (1) Convex analysis methods in learning the networks. He also has collaborations
and adaptive operator: The adaptive operator approach can with biologists seeking to discover the similarity and dissimilarity
be regarded as an extension of the proximal method in convex between the networks in computational models, and in the retinex
analysis. Each variable in the optimization is associated with an and the olfactory systems. (4) Image processing: In addition, Dr.
operator that distinguishes the variable from others. The operators Hwang studies fundamental image processing problems, such as
and the variables are updated at each iteration for optimal solutions. de-noising, de-blurring, segmentation, and compression problems,
Dr. Hwang studies the convergence and other mathematical using state-of-the-art analysis methods.
properties of the approach and has successfully applied it in source
separation problems and the sparse representation problem, even
with non-convex priors. (2) Frame-based compressive sensing
Publications 8. Jinn Ho and Wen-Liang Hwang, “Wavelet Bayesian Network Image
Denosing”, IEEE Trans. on Image Processing, Vol. 22, Issue 4, April
1. Stephane Mallat and Wen-Liang Hwang, “Singularity Detection 2013.
and Processing with Wavelets”, IEEE Transactions on Information
Theory, vol. 38, no. 2, pp. 617-645, March 1992. 9. Guan-Ju Peng and Wen-Liang Hwang, “Reweighted and Adaptive
Morphology Separation”, SIAM J. Imaging Sci. 7- 4, 2014, pp. 2048-
2. Rene Carmona, Wen-Liang Hwang, and Bruno Torresani, “Practical 2077.
Time-Frequency Analysis”, Academic Press, 1998.
10. Wen-Liang Hwang, Keng-Shih Lu and Jinn Ho, “Constrained
3. Ming-Shing Su, Wen-Liang Hwang, and Kuo-Yourn Cheng, “Analysis Null Space Component Analysis for Semiblind Source Separation
on Multiresolution Mosaic Images”, IEEE Transactions on Image Problem”, IEEE Transactions on Neural Networks and Learning
Processing, vol. 13, no. 7, pp. 952-959, July 2004. Systems, To Appear.
4. Chun-Liang Tu, Wen-Liang Hwang, and Jinn Ho, “Analysis of
Singularities from Modulus Maxima of Complex Wavelets”, IEEE
Transactions on Information Theory, vol. 51, no. 3, pp. 1049-1062,
March 2005.
5. Silong Peng and Wen-Liang Hwang, ‘’Adaptive Signal Decomposition
based on Local Narrow Band Signals”, IEEE Trans. on Signal
Processing, vol. 56, pp. 2669-2676, July 2008.
6. Silong Peng and Wen-Liang Hwang, “Null Space Pursuit: An
Operator-based Approach to Adaptive Signal Separation”, IEEE
Trans. on Signal Processing, vol. 58, pp. 2475-2483, May 2010.
7. Xiyuan Hu, Silong Peng, and Wen-Liang Hwang, “EMD Revisited: A
New Understanding of the Envelope and Resolving the Mode-Mixing
Problem in AM-FM Signals”, IEEE Trans. on Signal Processing,
March 2012.
79
