Computational Photography, Image Enhancement, Video Stablization
We work on developing computational approaches to enhance the quality of images taken under non-ideal conditions (e.g. images impaired by rain streaks) or expand users’ manipulation choices of the images (e.g. synthetic depth-of-field rendering, image stitching with large parallax, etc).
- Y. Li, R. T. Tan, X. Guo, J. Lu, and M. S. Brown, “Single Image Rain Streak Separation Using Layer Priors,” IEEE Trans. on Image Processing (TIP), vol. 26, no. 8, pp. 3874-3885, Aug. 2017. [pdf]
- K. Lin, N. Jiang, S. Liu, L.-F. Cheong, M. N. Do, and J. Lu, “Direct Photometric Alignment by Mesh Deformation,” IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, Jul. 2017. [pdf]
- K. Lin, N. Jiang, L.-F. Cheong, M. N. Do, and J. Lu, “SEAGULL: Seam-Guided Local Alignment for Parallax-Tolerant Image Stitching,” European Conf. Computer Vision (ECCV), Amsterdam, Oct. 2016. [pdf][supplementary]
- Y. Li, R. T. Tan, X. Guo, J. Lu, and M. S. Brown, “Rain Streak Removal Using Layer Priors,” IEEE Int. Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, Jun. 2016. [pdf]
- D. Vu, B. Chidester, H. Yang, M. N. Do, and J. Lu, “Efficient Hybrid Tree-Based Stereo Matching with Applications to Post-Capture Image Refocusing,” IEEE Trans. Image Processing (TIP), vol. 23, no. 8, pp. 3428-3442, Aug. 2014. [pdf][demo]