2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information

2021 IEEE International Conference on Acoustics, Speech and Signal Processing

6-11 June 2021 • Toronto, Ontario, Canada

Extracting Knowledge from Information
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Paper Detail

Paper IDCI-4.6
Paper Title ZERO-GRADIENT CONSTRAINTS FOR DESTRIPING OF REMOTE-SENSING DATA
Authors Kazuki Naganuma, Saori Takeyama, Shunsuke Ono, Tokyo Institute of Technology, Japan
SessionCI-4: Remote Sensing and Coded Aperture Imaging
LocationGather.Town
Session Time:Thursday, 10 June, 15:30 - 16:15
Presentation Time:Thursday, 10 June, 15:30 - 16:15
Presentation Poster
Topic Computational Imaging: [CIF] Computational Image Formation
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract This paper proposes an effective and efficient destriping method for remote-sensing data. Destriping of remote-sensing data is an essential task because stripe noise not only degrades the visual quality but also seriously affects subsequent processing. We formulate the destriping problem as a convex optimization problem involving zero-gradient constraints, where the constraints are designed to exploit the fact that the spatial and temporal gradients of stripe noise equal to zero. Our method imposes such strong constraints on stripe noise, and thus can fully capture the nature of stripe noise, leading to very effective destriping. Also, operations required for handling the zero-gradient constraints in optimization are simple, which enables us to develop an efficient algorithm for solving the problem by a primal-dual splitting method. We demonstrate the advantages of our method over existing methods on destriping experiments using remote-sensing data.