Dr. Xiaolin Wu – Faculty of Engineering
Xiaolin Wu

Dr. Xiaolin Wu


Image processing, multimedia coding and communication, computer vision, artificial intelligence

Areas of Specialization

Research Clusters

  • Professor

    Electrical & Computer Engineering


Xiaolin Wu’s research interests include image processing, data compression, digital multimedia, low-level vision, and network-aware visual communication. He has published over 350 research papers and holds four patents in these fields. His Google Scholar citation is over 19200 with an h-index of 66.

Dr. Wu is an IEEE fellow, and served on editorial boards of IEEE Transactions on Image Processing and IEEE Transactions on Multimedia. He is a member of IEEE Multimedia Signal Processing Executive Committee. He won an IS&T/SPIE Best Paper Award, a UWO distinguished research professor award, and a McMaster distinguished engineering professor award.

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Xiaolin Wu got his B.Sc. from Wuhan University, China in 1982, and Ph.D. from University of Calgary, Canada in 1988, both in computer science. Dr. Wu started his academic career in 1988, and has since been on the faculty of Western University, New York Polytechnic University (NYU-Poly), and joined McMaster University in 2002, where he is a professor at the Department of Electrical & Computer Engineering, and won a Distinguished Engineering Professor Award.

Also, Dr. Wu has extensive industrial research experiences. He was CTO of Ntec Media GmbH and collaborated with Microsoft, Nokia, Intel, IMAX and Huawei Canada. He held an NSERC senior industrial research chair, and is an IEEE fellow and a member of IEEE Industrial Signal Processing Executive Committee.

  • B.Sc. (Wuhan University, China)
  • Ph.D.(University of Calgary, Canada)
  • Dr. Wu’s CALIC algorithm won the first place in the JPEG lossless image coding competition, and was licensed to companies. The applications of CALIC include medical imaging, satellite imaging, prepress imaging, image archiving, and etc. Wu’s L3 (Layered Low-complexity Lossless) codec for digital cinema won first place in compression performance among seven proposals submitted to MPEG in July 2001. Wu’s fast color quantizer was incorporated into the independent JPEG group’s image compression software package (shareware), and widely used by graphics practitioners.
  • His ground-breaking work of temporal psychovisual modulation (TPVM) was featured in MIT Technology Review and in the Exploratory DSP column of IEEE Signal Processing Magazine.
  • Dr. Wu developed a family of image restoration algorithms for demosaicing, denoising, superresolution and deblurring.

X Zhang, X Wu, “LVQAC: Lattice Vector Quantization Coupled with Spatially Adaptive Companding for Efficient Learned Image Compression”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2023.

X Zhang, X Wu, “Multi-modality deep restoration of extremely compressed face videos”, IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (2), 2024-2037, 2022.

W. Chen, Y. Huang, M. Wang, X. Wu, X. Zeng, “Two-Stage Raw Denoising in the Dark”, EEE Transactions on Image Processing 32, 3679-3689, 2023.

Y Guo, X Wu, X Shu, “Data acquisition and preparation for dual-reference deep learning of image super-resolution”, IEEE Transactions on Image Processing 31, 4393-4404, 2022.

F Luo, X Wu, Y Guo, “Functional neural networks for parametric image restoration problems”, NIPS 2021.

SM Ayyoubzadeh, X Wu, “High frequency detail accentuation in CNN image restoration”, IEEE Transactions on Image Processing 30, 8836-8846 5, 2021.

Q Gao, X Wu, “Real-time deep image retouching based on learnt semantics dependent global transforms”, IEEE Transactions on Image Processing 30, 7378-7390, 2021.

X Zhang, X Wu, “Attention-guided image compression by deep reconstruction of compressive sensed saliency skeleton”, CVPR 2021.

Q Li, X Liu, J Jiang, C Guo, X Ji, X Wu, “Rapid whole slide imaging via dual-shot deep autofocusing”, IEEE Transactions on Computational Imaging 7, 124-136 9 2020.

X Zhang, X Wu, “Ultra High Fidelity Deep Image Decompression With l∞-Constrained Compression”, IEEE Transactions on Image Processing 30, 963-975 14 2020.

X Zhang, X Wu, “On Numerosity of Deep Neural Networks”, Advances in Neural Information Processing Systems 34 (NIPS 2020).

X Wu, D Gao, Q Chen, J Chen, “Multispectral imaging via nanostructured random broadband filtering”, Optics Express 28 (4), 4859-4875 16 2020.

X Wu, “A Linear Programming Approach for Optimal Contrast-Tone Mapping”, IEEE Transactions on Image Processing, 20 (5), 1262 – 1272.

G Zhai, X Wu, X Yang, W Lin, “A psychovisual quality metric in free-energy principle”, IEEE Transactions on Image Processing 21 (1), 41-52.

X Liu, X Wu, J Zhou, D Zhao, “Data-driven soft decoding of compressed images in dual transform-pixel domain”, IEEE Transactions on Image Processing 25 (4), 1649-1659, 2016.

X Wu, G Zhai, “Temporal Psychovisual Modulation: a new paradigm of information display”, IEEE Signal Processing Magazine 30 (1), 136-141, 2013.

X Wu, W Dong, X Zhang, G Shi, “Model-assisted adaptive recovery of compressed sensing with imaging applications”, IEEE Transactions on Image Processing 21 (2), 451-458, 2011.

X Wu, G Zhai, X Yang, W Zhang, “Adaptive sequential prediction of multidimensional signals with applications to lossless image coding”, IEEE Transactions on Image Processing 20 (1), 36-42, 2010.

X Zhang, X Wu, “Image interpolation by adaptive 2-D autoregressive modeling and soft-decision estimation”, IEEE transactions on image processing 17 (6), 887-896, 2008.

P Bao, L Zhang, X Wu,”Canny edge detection enhancement by scale multiplication”, IEEE transactions on pattern analysis and machine intelligence 27 (9), 1485-1490, 2005.

X Wu, N Memon, “Context-based, adaptive, lossless image coding”, IEEE transactions on Communications 45 (4), 437-444, 1997.

X Wu, “Adaptive split-and-merge segmentation based on piecewise least-square approximation”, IEEE transactions on pattern analysis and machine intelligence 15 (8), 808-815, 1993.

X Wu, “Color quantization by dynamic programming and principal analysis”, ACM Transactions on Graphics (TOG) 11 (4), 348-372, 1992.

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