Seeing Through Water

Alexei A. Efros, Volkan Isler, Jianbo Shi, and Mirko Visontai
Neural >Information Processing Systems (NIPS 17), 2004.

  • Adobe portable document format (pdf) (571KB)
Copyright notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. These works may not be reposted without the explicit permission of the copyright holder.

We consider the problem of recovering an underwater image distorted by surface waves. A large amount of video data of the distorted image is acquired. The problem is posed in terms of finding an undistorted image patch at each spatial location. This challenging reconstruction task can be formulated as a manifold learning problem, such that the center of the manifold is the image of the undistorted patch. To compute the center, we present a new technique to estimate global distances on the manifold. Our technique achieves robustness through convex flow computations and solves the ?eakage?problem inherent in recent manifold embedding techniques.

Associated Center(s) / Consortia: Vision and Autonomous Systems Center
Number of pages: 8

Text Reference
Alexei A. Efros, Volkan Isler, Jianbo Shi, and Mirko Visontai, "Seeing Through Water," Neural >Information Processing Systems (NIPS 17), 2004.

BibTeX Reference
   author = "Alexei A. Efros and Volkan Isler and Jianbo Shi and Mirko Visontai",
   title = "Seeing Through Water",
   booktitle = "Neural >Information Processing Systems (NIPS 17)",
   year = "2004",