Structural Compact Core Tensor Dictionary Learning for Multispec-Tral Remote Sensing Image Deblurring

The multispectral remote sensing image (MS-RSI) is blurred existing multispectral camera due to various hardware limitations. In this paper, we propose a novel structural compact core tensor dictionary learning (SCCTDL) model for MS-RSI deblurring. First, the multispectral patch is modeled by three-order tensor and high-order singular value decomposition is applied to the tensor. Then the task of MS-RSI deblurring is formulated as a minimum sparse core tensor estimation problem. To improve the accuracy of core tensor coding, the core tensor estimation based on the structural compact principle is introduced into the SCCTDL model to exploit abundant structural similarity in image. Experimental results suggest that our method outperforms several existing MS-RSI deblurring methods in both subjective image quality and visual perception.

• Publication year

  2018

• Venue

  International Conference on Information Photonics

• Publication date

  2018-10-01

• Fields of study

  Computer Science, Engineering, Environmental Science

• Identifiers
  DOI 10.1109/ICIP.2018.8451531
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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