Publications

Books

[B1] Stanley H. Chan, Introduction to Probability for Data Science, Michigan University Press, 2021.

[B2] Stanley H. Chan and Nicholas Chimitt, Computational Imaging through Atmospheric Turbulence, Now Publisher 2023.

[B3] Stanley H. Chan, ‘‘Tutorial on Diffusion Models for Imaging and Vision’’, To Appear, Now Publisher, 2024.

Journals

[J44] Guan Zhe Hong, Yin Cui, Ariel Fuxman, Stanley H. Chan, Enming Luo, ‘‘Why Fine-grained Labels in Pretraining Benefit Generalization?’’ Transactions on Machine Learning Research, Oct. 2024.

[J43] Aaron Hendrickson, David Haefner, Stanley H. Chan, Nicholas R. Shade, Eric R. Fossum, ‘‘PCH-EM: A solution to information loss in the photon transfer method’’, IEEE Transactions on Electron Devices, vol. 71, no. 8, pp. 4781-4788, Aug. 2024.

[J42] Nicholas Chimitt, Xingguang Zhang, Yiheng Chi, Stanley H. Chan, ‘‘Scattering and Gathering for Spatially Varying Blurs’’, IEEE Transactions on Signal Processing, vol. , pp. 1507-1517, Mar 2024.

[J41] Abhiram Gnanasambandam, Yash Sanghvi and Stanley H. Chan, ‘‘The Secrets of Non-Blind Poisson Deconvolution’’, IEEE Transactions on Computational Imaging, vol. 10, pp. 343-356, Feb 2024.

[J40] Xiangyu Qu, Yiheng Chi, Stanley H. Chan, ‘‘Spatially Varying Exposure with 2-by-2 Multiplexing: Optimality and Universality’’, IEEE Transactions on Computational Imaging, vol. 10, pp. 261-276, Jan 2024.

[J39] Xingguang Zhang, Zhiyuan Mao, Nicholas Chimitt, and Stanley H. Chan, ‘‘Imaging through the Atmosphere using Turbulence Mitigation Transformer’’, IEEE Transactions on Computational Imaging, vol. 10, pp. 115-128, Jan 2024. [Project Page]

[J38] Stanley H. Chan, ‘‘Computational Image Formation: Simulators in the Deep Learning Era’’, Journal of Imaging Science and Technology, vol. 67, pp.1-17, Nov 2023.

[J37] Nicholas Chimitt and Stanley H. Chan, ‘‘Anisoplanatic Optical Turbulence Simulation for Near-Continuous Profiles without Wave Propagation’’, SPIE Optical Engineering (SPIE OpEng), vol. 62, no. 7, pp. 078103, Jul. 2023.

[J36] Chengxi Li, Stanley H Chan, Yi-Ting Chen, ‘‘Driver-centric Risk Object Identification’’, IEEE Trans. Pattern Recognition and Machine Intelligence, vol. 45, no. 11, pp. 13683-13698, Nov. 2023.

[J35] Yunping Zhang, Stanley H Chan, Edmund Y Lam, ‘‘Photon-starved snapshot holography’’, APL Photonics, vol. 8, no. 5, pp. 056106, May 2023.

[J34] Stanley H. Chan, ‘‘On the Insensitivity of Bit Density to Read Noise in One-bit Quanta Image Sensors’’, IEEE Sensors Journal, vol. 23, no. 4, pp. 3666-3674, Feb. 2023.

[J33] Nicholas Chimitt, Xingguang Zhang, Zhiyuan Mao, and Stanley H. Chan, ‘‘Real- Time Dense Field Phase-to-Space Simulation of Imaging through Atmospheric Turbulence’’, IEEE Trans. Computational Imaging, vol. 8, pp. 1159-1169, Dec. 2022.

[J32] Yash Sanghvi, Abhiram Gnanasambandam, Zhiyuan Mao, Stanley H. Chan, ‘‘Photon-Limited Blind Deconvolution using Unsupervised Iterative Kernel Estimation’’, IEEE Trans. Computational Imaging, vol. 8, pp. 1051-1062, Dec. 2022. [CODE]

[J31] Xue Zhang, Gene Cheung, Jiahao Pang, Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan, ‘‘Graph-Based Depth Denoising and Dequantization for Point Cloud Enhancement’’, IEEE Trans. Image Processing, vol. 31, pp. 6863-6878, Sep. 2022.

[J30] Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan, ‘‘Photon-Limited Non-Blind Deblurring Using Algorithm Unrolling’’, IEEE Trans. Computational Imaging, vol. 8, pp. 851- 864, Sep. 2022. [Project Page]

[J29] Stanley H. Chan, ‘‘What Does a One-Bit Quanta Image Sensor Offer?’’, IEEE Trans. Computational Imaging, vol. 8, pp. 770-783, Aug. 2022. [CODE]

[J28] Stanley H. Chan, ‘‘Tilt-then-Blur or Blur-then-Tilt? Clarifying the Atmospheric Turbulence Model’’, IEEE Signal Processing Letters, vol. 29, pp. 1833-1837, Aug. 2022.

[J27] Abhiram Gnanasambandam and Stanley H. Chan, ‘‘Exposure-Referred Signal-to-Noise Ratio for Digital Image Sensors’’, IEEE Trans. Computational Imaging, vol. 8, pp.561-575, Jun. 2022.

[J26] Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka, Stanley H Chan, Yonina C Eldar, ‘‘Graph Signal Restoration Using Nested Deep Algorithm Unrolling’’, IEEE Trans. Signal Processing, vol. 70, pp. 3296-3311, Jun 2022.

[J25] Jiaju Ma, Stanley H. Chan, Eric R. Fossum, ‘‘Review of Quanta Image Sensors for Ultra-Low-Light Imaging’’, IEEE Journal of Electron Devices, vol. 69, no. 6, pp.2824-2839, Jun 2022.

[J24] Omar Elgendy, Abhiram Gnanasambandam, Stanley H. Chan, and Jiaju Ma ‘‘Low-light demosaicking and denoising for small pixels using learned frequency selection’’, IEEE Trans. Computational Imaging, vol. 7, pp. 137-150, Jan 2021.

[J23] Abhiram Gnanasambandam and Stanley H. Chan, ‘‘HDR imaging with Quanta Image Sensors: Theoretical limits and optimal reconstruction’’, IEEE Trans. Computational Imaging, vol. 6, pp. 1571-1585, 2020. [CODE]

[J22] Zhiyuan Mao, Nicholas Chimitt, and Stanley H. Chan, ‘‘Image reconstruction of static and dynamic scenes through anisoplanatic turbulence’’, IEEE Trans. Computational Imaging, vol. 6, pp. 1415-1428, Oct. 2020.

[J21] Xiran Wang, Jason Juang, Stanley H. Chan, ‘‘Automatic foreground extraction from imperfect backgrounds using Multi-Agent Consensus Equilibrium’’, Journal of Visual Communication and Image Representation, volume 72, Oct. 2020, 102907.

[J20] Nicholas Chimitt and Stanley H. Chan, ‘‘Simulating anisoplanatic turbulence by sampling inter-modal and spatially correlated Zernike coefficients’’, Optical Engineering, 59(8), 083101, July 2020.

[J19] Rizwan Ahmad, Charles A. Bouman, Gregery T. Buzzard, Stanley H. Chan, Edward T. Reehorst, Philip Schniter, ‘‘Plug-and-Play methods for magnetic resonance imaging’’, IEEE Signal Processing Magazine, vol. 37, no. 1, 105-116, Jan. 2020.

[J18] Omar A. Elgendy, and Stanley H. Chan, ‘‘Color filter arrays design for quanta image sensor’’, IEEE Trans. Computational Imaging, vol. 6, pp. 652-665, Jan. 2020.

[J17] Abhiram Gnanasambandam, Omar A. Elgendy, Jiaju Ma, and Stanley H. Chan, ‘‘Megapixel photon-counting color imaging using quanta image sensor’’, OSA Optics Express, vol. 27, no. 12, pp. 17298-17310, Jun. 2019. [CODE]

[J16] Stanley H. Chan, ‘‘Performance analysis of Plug-and-Play ADMM: A graph signal processing perspective’’, IEEE Trans. Computational Imaging, vol. 5, no. 2, pp. 274-286, Jun. 2019.

[J15] Joon Hee Choi, Omar A. Elgendy and Stanley H. Chan, ‘‘Optimal combination of image denoisers’’, IEEE Trans. Image Process., vol. 28, no. 8, pp. 4016-4031, Aug. 2019. (CODE) (Supplementary Material)

[J14] Gregery T. Buzzard, Stanley H. Chan, Suhas Sreehari and Charles A. Bouman ‘‘Plug-and-Play unplugged: Optimization free reconstruction using consensus equilibrium’’, SIAM Journal on Imaging Science, vol. 11, no. 3, pp.2001-2020, Sep. 2018. [CODE]

[J13] Omar A. Elgendy and Stanley H. Chan, ‘‘Optimal threshold design for quanta image sensor’’, IEEE Trans. Computational Imaging, vol. 4, no. 1, Mar. 2018, pp. 99-111. [CODE]

[J12] Stanley H. Chan, Todd Zickler, and Yue M. Lu, ‘‘Understanding symmetric smoothing filters: A Gaussian mixture model perspective’’, IEEE Trans. Image Process., vol. 26, no. 11, pp. 5107-5121, Nov. 2017.

[J11] (IEEE SPS Best Paper Award 2022) Stanley H. Chan, Xiran Wang, and Omar Elgendy, ‘‘Plug-and-Play ADMM for image restoration: Fixed point convergence and applications’’, IEEE Trans. Computational Imaging, vol. 3, no. 5, pp.84-98, Mar. 2017. [CODE]

[J10] Stanley H. Chan, Omar Elgendy and Xiran Wang, ‘‘Images from bits: Non-iterative image reconstruction for quanta image sensors’’, MDPI Sensors Special Issue on Photon-Counting Image Sensors, vol. 16, no. 11, paper 1961, pp.1-21, Nov. 2016. [CODE]

[J9] Enming Luo, Stanley H. Chan, and Truong Q. Nguyen, ‘‘Adaptive image denoising by mixture adaptation’’, IEEE Trans. Image Process., vol. 25, no. 10., pp.4489-4503, Oct. 2016. [CODE]

[J8] Chien-Sheng Liao, Joon Hee Choi, Delong Zhang, Stanley H. Chan, and Ji-xin Cheng, ‘‘Denoising stimulated Raman spectroscopic images by total variation minimization’’, Journal of Physical Chemistry C, vol. 119, no. 33, pp.19397-19403, Jul. 2015. [CODE]

[J7] Enming Luo, Stanley H. Chan, and Truong Q. Nguyen, ‘‘Adaptive image denoising by targeted databases’’, IEEE Trans. Image Process., vol. 24, no. 7, pp.2167-2181, Jul. 2015. [CODE]

[J6] Lee-Kang Liu, Stanley H. Chan, and Truong Q. Nguyen, ‘‘Depth Reconstruction from Sparse Samples: Representation, algorithm, and sampling’’, IEEE Trans. Image Process., vol. 24, no. 6, pp. 1983-1996, Jun. 2015. [CODE]

[J5] Stanley H. Chan, Todd Zickler, and Yue M. Lu, ‘‘Monte Carlo non-local means: Random sampling for large-scale image filtering’’, IEEE Trans. Image Process., vol. 23, no. 8, pp. 3711-3725, Aug. 2014. [CODE]

[J4] Stanley H. Chan, Ramsin Khoshabeh, Kristofor B. Gibson, Philip E. Gill and Truong Q. Nguyen, ‘‘An augmented Lagrangian method for total variation video restoration,’’ IEEE Trans. Image Process., vol. 20, issue 11, pp.3097-3111, Nov. 2011. [CODE]

[J3] Stanley H. Chan and Truong Q. Nguyen, ‘‘LCD motion blur: modeling, analysis and algorithm,’’ IEEE Trans. Image Process., vol.20, issue 8, pp.2352-2365, Aug. 2011.

[J2] Stanley H. Chan, Thomas X. Wu and Truong Q. Nguyen, ‘‘Comparison of two frame rate conversion schemes for reducing LCD motion blurs,’’ IEEE Signal Process. Letters, vol. 17, issue 9, pp.783-786, Sep. 2010.

[J1] Stanley H. Chan, Alfred K. Wong, and Edmund Y. Lam, ‘‘Initialization for robust inverse synthesis of phase-shifting masks in optical projection lithography,’’ Optics Express, vol. 16, no. 9, pp.14746-14760, Sep. 2008.

Selective Conferences

(Selective conference = Conference papers with 8+ page in length, usually (though not always) with an acceptance rate of 25%.)

[H25] Kent Gauen, Stanley H. Chan, ‘‘Soft Superpixel Neighborhood Attention’’ Neural Information Processing Systems (NeurIPS), accepted, 2024.

[H24] Prateek Chennuri, Yiheng Chi, Enze Jiang, G. M. Dilshan Godaliyadda, Abhiram Gnanasambandam, Hamid R. Sheikh, Istvan Gyongy, and Stanley H. Chan, ‘‘Quanta Video Restoration’’, European Conference on Computer Vision (ECCV), accepted, 2024. Project Page

[H23] Yash Sanghvi, Yiheng Chi, and Stanley H. Chan, ‘‘Kernel Diffusion: An Alternate Approach to Blind Deconvolution’’, European Conference on Computer Vision (ECCV), accepted, 2024.

[H22] Stanley H. Chan, Hashan K Weerasooriya, Weijian Zhang, Pamela Abshire, Istvan Gyongy, Robert K Henderson, ‘‘Resolution Limit of Single-Photon LIDAR’’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), accepted, 2024.

[H21] Vishal Purohit, Junjie Luo, Yiheng Chi, Qi Guo, Stanley H. Chan, Qiang Qiu, ‘‘Generative Quanta Color Imaging’’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), accepted, 2024.

[H20] Xingguang Zhang, Nicholas Chimitt, Yiheng Chi, Zhiyuan Mao, Stanley H. Chan, ‘‘Spatio-Temporal Turbulence Mitigation: A Translational Perspective’’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), accepted, 2024. [Project Page]

[H19] Feng Liu et al., ‘‘FarSight: A Physics-Driven Whole-Body Biometric System at Large Distance and Altitude’’ IEEE Winter Conference on Computer Vision (WACV), pp. 6227-6236, 2024.

[H18] Ajay Jaiswal, Xingguang Zhang, Stanley H. Chan, Zhangyang Wang, ‘‘Physics-Driven Turbulence Image Restoration with Stochastic Refinement’’, IEEE International Conference on Computer Vision (ICCV), pp. 12170-12181, 2023.

[H17] Yiheng Chi, Xingguang Zhang, and Stanley H. Chan, ‘‘HDR Imaging with Spatially Varying Signal-to-Noise Ratios’’, IEEE Conf. Computer Vision Pattern Recognition (CVPR), pp. 5724-5734, 2023.

[H16] Yash Sanghvi, Zhiyuan Mao, and Stanley H. Chan, ‘‘Structured Kernel Estimation for Photon-Limited Deconvolution’’, IEEE Conf. Computer Vision Pattern Recognition (CVPR), pp. 9863-9872, 2023. (Video) [Project Page]

[H15] Hyung-gun Chi, Seunggeun Chi, Stanley Chan, Karthik Ramani, ‘‘Pose Relation Transformer Refine Occlusions for Human Pose Estimation’’, IEEE International Conference on Robotics and Automation (ICRA), London, United Kingdom, 2023, pp. 6138-6145

[H14] Zhiyuan Mao, Ajay Jaiswal, Atlas Wang, and Stanley H. Chan, ‘‘Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics- Inspired Transformer Model’’, European Conference on Computer Vision (ECCV), 2022. [CODE]

[H13] Chengxi Li, Xiangyu Qu, Abhiram Gnanasambandam, Omar A. Elgendy, Jiaju Ma, Stanley H. Chan, ‘‘Photon-Limited Object Detection using Non-local Feature Matching and Knowledge Distillation’’, IEEE International Conference on Computer Vision Workshop (ICCV-W), 2021, pp. 3976-3987.

[H12] Xiangyu Qu, and Stanley H. Chan, ‘‘Detecting and Segmenting Adversarial Graphics Patterns from Images’’, IEEE International Conference on Computer Vision Workshop (ICCV-W), 2021, pp. 71-80.

[H11] Abhiram Gnanasambandam, Alex M. Sherman, and Stanley H. Chan, ‘‘Optical Adversarial Attack’’, IEEE International Conference on Computer Vision Workshop (ICCV-W), 2021, pp. 92-101.

[H10] Zhiyuan Mao, Nicholas Chimitt, and Stanley H. Chan‘‘Accelerating Atmospheric Turbulence Simulation via Learned Phase-to-Space Transform’’, IEEE International Conference on Computer Vision (ICCV), 2021, pp. 14759-14768. [CODE]

[H9] Guanzhe Hong, Zhiyuan Mao, Xiaojun Lin, and Stanley H. Chan ‘‘Student-teacher learning from clean inputs to noisy inputs’’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

[H8] Yiheng Chi, Abhiram Gnanasambandam, Vladlen Koltun, and Stanley H. Chan ‘‘Dynamic low-light imaging with Quanta Image Sensors’’, European Conference on Computer Vision (ECCV), pp. 122-138, 2020 .

[H7] Abhiram Gnanasambandam, and Stanley H. Chan ‘‘Image classification in the dark using Quanta Image Sensors’’, European Conference on Computer Vision (ECCV), pp. 484-501, 2020. [CODE]

[H6] Chengxi Li, Stanley H. Chan, Yi-Ting Chen, ‘‘Who Make Drivers Stop? Towards Driver-centric Risk Assessment: Risk Object Identification via Causal Inference’’, IEEE International Conference on Intelligent Robots and Systems (IEEE IROS), 2020.

[H5] Chengxi Li, Yue Meng, Stanley H. Chan, Yi-Ting Chen, ‘‘Learning 3d-aware egocentric spatial-temporal interaction via graph convolutional networks’’, IEEE International Conference on Robotics and Automation (IEEE ICRA), pp. 8418-8424, 2020.

[H4] Abhiram Gnansambandam and Stanley H. Chan, ‘‘One size fits all: Can we train one denoiser for all noise levels?’’, Journal of Machine Learning Research Workshop and Conference Proceedings (ICML), pp. 1412-1422, 2020. (Video)

[H3] Nicholas Chimitt and Stanley H. Chan, ‘‘Simulating anisoplanatic turbulence by sampling correlated Zernike coefficients’’, IEEE International Conference on Computational Photography (ICCP) 2020. (Video) [CODE]

[H2] Stanley H. Chan and Edoardo M. Airoldi, ‘‘A consistent histogram estimator for exchangeable graph models’’, Journal of Machine Learning Research Workshop and Conference Proceedings, vol. 32, no. 1, pp. 208-216, 2014. [CODE]

[H1] EdoardoM. Airoldi, Thiago B. Costa and Stanley H. Chan, ‘‘Stochastic blockmodel approx- imation of a graphon: Theory and consistent estimation,’’ Advances in Neural Information Processing Systems (NeurIPS), pp.692–700, Dec. 2013. [CODE]

Conference

(Peer-reviewed, typically 4 pages short papers. Does not include extended abstracts.)

[C33] Weijian Zhang, Hashan K. Weerasooriya, Prateek Chennuri, Stanley H. Chan, ‘‘Parametric Modeling and Estimation of Photon Registrations for 3D Imaging’’, IEEE MMSP 2024.

[C32] William C Yau, Weijian Zhang, Hashan Kavinga Weerasooriya, Stanley H Chan, ‘‘Analysis and Improvement of Rank-Ordered Mean Algorithm in Single-Photon LiDAR’’, IEEE MMSP 2024.

[C31] Yash Sanghvi, Abhiram Gnanasambandam, Stanley H. Chan, ‘‘Photon Limited Non-Blind Deblurring Using Algorithm Unrolling’’, IEEE ICASSP, 2022. (A longer version is on arXiv. Also presented as an extended abstract in NeurIPS workshop 2021.)

[C30] Masatoshi Nagahama, Koki Yamada, Yuichi Tanaka, Stanley H Chan, Yonina C Eldar, ‘‘Graph signal denoising using nested-structured deep algorithm unrolling’’, IEEE ICASSP, 2021.

[C29] Abhiram Gnanasambandam, Jiaju Ma, and Stanley H. Chan, ‘‘High dynamic range imaging with quanta image sensor’’, International Image Sensor Workshop (IISW), Snowbird, Utah, Jun. 2019. Paper R23.

[C28] Jiaju Ma, Yu-Wing Chung, Abhiram Gnanasambandam, Stanley H. Chan, and Saleh Masoodian, ‘‘Photon-counting imaging with multi-bit quanta image sensor’’, International Image Sensor Workshop (IISW), Snowbird, Utah, Jun. 2019. Paper R19.

[C27] Yoshinao Yazaki, Yuichi Tanaka, and Stanley H. Chan, ‘‘Interpolation and denoising of graph signals using Plug-and-Play ADMM’’, IEEE ICASSP, pp. 5431-5435, Brighton, United Kingdom, May 2019.

[C26] Joon Hee Choi, Omar A. Elgendy and Stanley H. Chan, ‘‘Image reconstruction for Quanta Image Sensors using deep neural networks’’, IEEE ICASSP, pp. 6543-6547, Calgary, Canada, Apr. 2018.

[C25] Yiheng Chi and Stanley H. Chan, ‘‘Fast and robust recursive filter for image denoising’’, IEEE ICASSP, pp. 1708- 1712, Calgary, Canada, Apr. 2018. [CODE]

[C24] Xiran Wang and Stanley H. Chan, ‘‘Parameter-free Plug-and-Play ADMM for image restoration’’, IEEE ICASSP, pp.1323-1327, New Orleans, Louisiana, Mar. 2017.

[C23] Chiman Kwan, Joon Hee Choi, Stanley H. Chan, Jin Zhou, and Bence Budavari, ‘‘Resolution enhancement for hyperspectral images: A super-resolution and fusion approach’’, IEEE ICASSP, pp.6180-6184, New Orleans, Louisiana, Mar. 2017.

[C22] (Best Paper Award) Omar Elgendy and Stanley H. Chan, ‘‘Image reconstruction and threshold design for quanta image sensors’’, IEEE ICIP, pp.978-982, Phoenix, Arizona, Sep. 2016.

[C21] Stanley H. Chan, Enming Luo, and Truong Q. Nguyen, ‘‘Adaptive patch-based image denoising by EM adaptation’’, IEEE GlobalSIP, pp. 810-814, Orlando, Florida, Dec. 2015.

[C20] Stanley H. Chan, Todd Zickler, Yue M. Lu, ‘‘Understanding symmetric smoothing filters via Gaussian mixtures’’, IEEE ICIP, pp. 2500-2504, Quebec City, Canada, Sep. 2015.

[C19] Stanley H. Chan, and Yue M. Lu, ‘‘Efficient image reconstruction for giga-pixel quantum image sensors’’, IEEE GlobalSIP, pp. 312-316, Atlanta, Georgia, Dec. 2014.

[C18] Enming Luo, Stanle H. Chan, and Truong Q. Nguyen, ‘‘Image Denoising by Targeted External Databases’’, IEEE ICASSP, pp. 3019-3023, Florence, Italy, May 2014.

[C17] Stanley H. Chan, Thiago B. Costa and Edoardo M. Airoldi, ‘‘Estimation of exchangeable random graph models by stochastic blockmodel approximation,’’ IEEE GlobalSIP, pp.293-296, Austin, Texas, Dec. 2013.

[C16] Enming Luo, Stanley H. Chan, Shenjun Pan and Truong Q. Nguyen, ‘‘Adaptive non- local means for multiview image denoising - searching for the right patches via a statistical approach,’’ IEEE ICIP, Melbourne, Australia, Sep. 2013.

[C15] Stanley H. Chan, Todd Zickler and Yue M. Lu, ‘‘Fast non-local filtering by random sampling: it works, especially for large images,’’ IEEE ICASSP, pp.1603-1607, Vancouver, Canada, May 2013.

[C14] Daniel Pipa, Stanley H. Chan and Truong Q. Nguyen, ‘‘Directional decomposition based total variation image restoration,’’ EUSIPCO, pp. 1558-1562, Bucharest, Romania, Aug. 2012.

[C13] Lee-Kang Liu, Stanley H. Chan and Truong Q. Nguyen, ‘‘Do we really need Gaussian filters for feature point detection?’’ EUSIPCO, pp. 131-135, Bucharest, Romania, Aug. 2012.

[C12] Stanley H. Chan and Truong Q. Nguyen, ‘‘Single-image, two-layered, out-of-focus blur removal,’’ Proc. SPIE 8500, pp.1 - 15, San Diego, California, Aug. 2012.

[C11] Stanley H. Chan and Truong Q. Nguyen, ‘‘Single image spatial-variant out-of-focus blur removal,’’ IEEE ICIP, pp.677- 680, Brussels, Belgium, Sep. 2011.

[C10] Stanley H. Chan, Ankit K. Jain, Truong Q. Nguyen and Edmund Y. Lam, ‘‘Bounds for the condition numbers of spatially variant convolution matrices in image restoration problems,’’ OSA Topical Meeting in Signal Recovery and Synthesis (OSA-SRS), Paper SMA4, Jul. 2011.

[C9] Ramsin Khoshabeh, Stanley H. Chan and Truong Q. Nguyen, ‘‘Spatiotemporal consistency in video disparity estimation,’’ IEEE ICASSP, pp.885-888, Prague, Czech Republic, May 2011.

[C8] Stanley H. Chan, Ramsin Khoshabeh, Kristofor B. Gibson, Philip E. Gill and Truong Q. Nguyen, ‘‘An augmented Lagrangian method for video restoration,’’ IEEE ICASSP, pp. 941-944, Prague, Czech Republic, May 2011.

[C7] Stanley H. Chan, ‘‘Constructing a sparse convolution matrix for shift varying image restoration problems,’’ IEEE ICIP, pp.3601-3604, Hong Kong, Sep. 2010.

[C6] Stanley H. Chan and Truong Q. Nguyen, ‘‘LCD motion blur modeling and simulation,’’ IEEE ICME, pp.400-405, Singapore, Jul. 2010.

[C5] Stanley H. Chan, Dung Vo and Truong Q. Nguyen, ‘‘Sub-pixel motion estimation without interpolation,’’ IEEE ICASSP, pp.722-725, Dallas, Texas, Mar. 2010.

[C4] Shay Har-Noy, Stanley H. Chan and Truong Q. Nguyen, ‘‘Demosaicing images with mo- tion blur,’’ IEEE ICASSP, pp.1006-1009, Dallas, Texas, Mar. 2010.

[C3] Stanley H. Chan and Truong Q. Nguyen, ‘‘Fast LCD motion deblurring by decimation and optimization,’’ IEEE ICASSP, pp.1201-1204, Taipei, Taiwan, Apr. 2009.

[C2] Stanley H. Chan and Edmund Y. Lam, ‘‘Inverse image problem of designing phase shifting masks in optical lithography,’’ IEEE ICIP, pp.1832-1835, San Diego, California, Oct. 2008.

[C1] Stanley H. Chan, Alfred K.Wong, and Edmund Y. Lam, ‘‘Inverse synthesis of phase shifting mask for optical lithography,’’ in OSA Topical Meeting in Signal Recovery and Synthesis (OSA-SRS), SMD3, Jun. 2007.