Publications

In Press
S. H. Chan, T. Zickler, and Y. M. Lu, “Monte Carlo non local means: Random sampling for large-scale image filtering,” IEEE Trans. Image Process. In Press. Publisher's Version 1312.7366v1.pdf
Submitted
E. Luo, S. H. Chan, and T. Q. Nguyen, “Adaptive Image Denoising by Targeted Databases,” IEEE Trans. Image Process. Submitted. Publisher's Version
L. - K. Liu, S. H. Chan, and T. Q. Nguyen, “Sparse Reconstruction of Depth Data: Representation, Algorithm, and Sampling,” IEEE Trans. Image Process. Submitted. Publisher's Version
2014
E. Luo, S. H. Chan, and T. Q. Nguyen, “Image Denoising by Targeted External Databases,” in Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, 2014, pp. 3019-3023. luo_chan_nguyen_2014.pdf
S. H. Chan and E. 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. chan_airoldi_2014.pdf
2013
S. H. Chan, T. Zickler, and Y. M. Lu, “Fast non-local filtering by random sampling: it works, especially for large images,” in Proceedings of IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP '13), Vancouver, Canada, 2013, pp. 1603-1607. chan_zickler_lu_2013.pdf
S. H. Chan, T. B. Costa, and E. M. Airoldi, “Estimation of exchangeable graph models by stochastic block approximation,” Proceedings of 1st IEEE Global conference on Signal Information Processing (GlobalSIP '13). pp. 293-296, 2013. chan_costa_airoldi_2013.pdf
E. M. Airoldi, T. B. Costa, and S. H. Chan, “Stochastic blockmodel approximation of a graphon: Theory and consistent estimation,” Advances in Neural Information Processing Systems (NIPS '13), vol. 26, pp. 398.1-398.9, 2013. 1311.1731v1.pdf
E. Luo, S. H. Chan, S. Pan, and T. Q. Nguyen, “Adaptive Non-local Means for Multiview Image Denoising - Searching for the Right Patches via a Statistical Approach,” Proceedings of IEEE International Conference on Image Processing (ICIP '13). 2013. luo_chan_pan_nguyen_2013.pdf
2012
S. H. Chan and T. Q. Nguyen, “Single-image, two-layered, out-of-focus blur removal,” SPIE Image Reconstruction from Incomplete Data VII. SPIE, pp. 8500-15, 2012. WebsiteAbstract

This paper addresses the problem of two-layer out-of-focus blur removal from a single image, in which either the foreground or the background is in focus while the other is out of focus. To recover details from the blurry parts, the existing blind deconvolution algorithms are insufficient as the problem is spatially variant. The proposed method exploits the invariant structure of the problem by first predicting the occluded background. Then a blind deconvolution algorithm is applied to estimate the blur kernel and a coarse estimate of the image is found as a side product. Finally, the blurred region is recovered using total variation minimization, and fused with the sharp region to produce the final deblurred image.

chan_nguyen_2012.pdf chan_nguyen_2012_supplementary.pdf
L. Liu, S. H. Chan, and T. Q. Nguyen, “Do We Really Need Gaussian Filters for Feature Point Detection?20th European Signal Processing Conference (EUSIPCO). pp. 131-135, 2012. Project WebsiteAbstract

This paper studies the issue of which filters should be used for
feature point detection. Classical feature point detection methods,
e.g., SIFT, are based on the scale-space theory in which
Gaussian filters are proven to be optimal under the scale-space axiom. However, the recent method SURF demonstrates empirically that a box filter can also achieve good performance even though it violates the scale-space axiom. This leads to the question: Is Gaussian filters necessary for feature point detection? Based on the analysis using filter bank and detection theory, we show that theoretically it is possible for a box filter to perform better than the Gaussian filter. Additionally, we show that a new filter, pyramid filter, performs better than both box and Gaussian filters in some situations.

liu_chan_nguyen_2012.pdf liu_chan_nguyen_2012_supplementary.pdf
D. Pipa, S. H. Chan, and T. Q. Nguyen, “Directional Decomposition Based Total Variation Image Restoration,” 20th European Signal Processing Conference (EUSIPCO). pp. 1558-1562, 2012.Abstract

This paper proposes an extension of total variation (TV) image deconvolution technique that enhances image quality over classical TV while preserving algorithm speed. Enhancement is achieved by altering the regularization term to include directional decompositions before the gradient operator. Such decompositions select areas of the image with characteristics that are more suitable for a certain type of gradient than another. Speed is guaranteed by the use of the augmented Lagrangian approach as basis for the algorithm. Experimental evidence that the proposed approach improves TV deconvolution is provided, as well as an outline for a future work aiming to support and substantiate the proposed method.

pipa_chan_nguyen_2012.pdf
2011
S. H. Chan, R. Khoshabeh, K. B. Gibson, P. E. Gill, and T. Q. Nguyen, “An augmented Lagrangian method for video restoration,” Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '11). IEEE, pp. 941-944, 2011. chan_khoshabeh_gibson_2011_icassp.pdf
R. Khoshabeh, S. H. Chan, and T. Q. Nguyen, “Spatio-temporal consistency in video disparity estimation,” Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '11). pp. 885-888, 2011. Project Page khoshabeh_chan_nguyen_2011.pdf
S. H. Chan, A. K. Jai, T. Q. Nguyen, and E. Y. Lam, “Bounds for the condition numbers of spatially-variant convolution matrices in image restoration problems,” Signal Recovery and Synthesis. OSA, pp. SMA4, 2011. chan_jain_nguyen_lam_2011.pdf
S. H. Chan and T. Q. Nguyen, “Single image spatial variant out-of-focus blur removal,” Proceedings of IEEE International Conference on Image Processing (ICIP '11). IEEE, pp. 677-680, 2011. chan_nguyen_2011_icip.pdf
S. H. Chan and T. Q. Nguyen, “LCD motion blur: modeling, analysis and algorithm,” IEEE Transactions on Image Processing, vol. 20, no. 8, pp. 2352-2365, 2011. Project Page chan_nguyen_2011.pdf
S. H. Chan, R. Khoshabeh, K. B. Gibson, P. E. Gill, and T. Q. Nguyen, “An augmented Lagrangian method for total variation video restoration,” IEEE Transactions on Image Processing, vol. 20, no. 11, pp. 3097-3111, 2011. Project Page chan_khoshabeh_gibson_2011.pdf
2010
S. Har-Noy, S. H. Chan, and T. Q. Nguyen, “Demosaicing images with motion blur,” Proceedings of IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP '10). pp. 1006-1009, 2010.
S. H. Chan, D. Vo, and T. Q. Nguyen, “Sub-pixel motion estimation without interpolation,” Proceedings of IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP '10). pp. 722-725, 2010. chan_vo_nguyen_2010.pdf

Pages