%Aigaion2 BibTeX export from Idiap Publications
%Thursday 21 November 2024 12:30:29 PM

@ARTICLE{Chan_IEEETRANS.COMPUT.IMAG._2016,
         author = {Chan, Kevin G. and Streichan, Sebastian J. and Trinh, Le A. and Liebling, Michael},
       keywords = {Fluorescence Microscopy, image denoising, image reconstruction, motion blur, Temporal superresolution},
       projects = {Idiap},
          title = {Simultaneous temporal superresolution and denoising for cardiac fluorescence microscopy},
        journal = {IEEE Transactions on Computational Imaging},
           year = {2016},
           note = {in press},
           issn = {2333-9403},
            url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7488256},
            doi = {10.1109/TCI.2016.2579606},
       abstract = {Due to low light emission of fluorescent samples, live fluorescence microscopy imposes a tradeoff between spatiotemporal resolution and signal-to-noise ratio. This can result in images and videos containing motion blur or Poisson-type shot noise, depending on the settings used during acquisition. Here, we propose an algorithm to simultaneously denoise and temporally super-resolve movies of repeating microscopic processes that is compatible with any conventional microscopy setup that can achieve imaging at a rate of at least twice that of the fundamental frequency of the process (above 4 frames per second for a 2 Hz process). Our method combines low temporal resolution frames from multiple cycles of a repeating process to reconstruct a denoised, higher temporal resolution image sequence which is the solution to a linear program that maximizes the consistency of the reconstruction with the measurements, under a regularization constraint. This paper describes, in particular, a parallelizable superresolution reconstruction algorithm and demonstrates its application to live cardiac fluorescence microscopy. Using our method, we experimentally show temporal resolution improvement by a factor of 1.6, resulting in a visible reduction of motion blur in both on-sample and off-sample frames.},
            pdf = {https://publications.idiap.ch/attachments/papers/2016/Chan_IEEETRANS.COMPUT.IMAG._2016.pdf}
}