Journal of Mathematical Imaging and Vision

, Volume 59, Issue 3, pp 373–393 | Cite as

Detecting Curved Edges in Noisy Images in Sublinear Time

  • Yi-Qing Wang
  • Alain Trouvé
  • Yali Amit
  • Boaz Nadler


Detecting edges in noisy images is a fundamental task in image processing. Motivated, in part, by various real-time applications that involve large and noisy images, in this paper we consider the problem of detecting long curved edges under extreme computational constraints, that allow processing of only a fraction of all image pixels. We present a sublinear algorithm for this task, which runs in two stages: (1) a multiscale scheme to detect curved edges inside a few image strips; and (2) a tracking procedure to estimate their extent beyond these strips. We theoretically analyze the runtime and detection performance of our algorithm and empirically illustrate its competitive results on both simulated and real images.


Curved edge detection Sublinear algorithm Noisy images 



Funding was provided by Division of Mathematical Sciences (Grant No. 0706816 ).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Applied MathematicsWeizmann Institute of ScienceRehovotIsrael
  2. 2.CMLA, ENS Cachan, CNRS, Université Paris-SaclayCachanFrance
  3. 3.Departments of Statistics, Computer Science and the CollegeUniversity of ChicagoChicagoUSA

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