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    Rights statement: This is the author’s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, 145, Part A, 2018 DOI: 10.1016/j.isprsjprs.2018.01.003

    Accepted author manuscript, 2 MB, PDF-document

    Embargo ends: 17/02/19

    Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License

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One-Two-One Network for Compression Artifacts Reduction in Remote Sensing

Research output: Contribution to journalJournal article

E-pub ahead of print
  • Baochang Zhang
  • Jiaxin Gu
  • Chen Chen
  • Jungong Han
  • Xiangbo Su
  • Xianbin Cao
  • Jianzhuang Liu
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<mark>Journal publication date</mark>11/2018
<mark>Journal</mark>ISPRS Journal of Photogrammetry and Remote Sensing
Issue numberPart A
Volume145
Number of pages13
Pages (from-to)184-196
StateE-pub ahead of print
Early online date17/02/18
Original languageEnglish

Abstract

Compression artifacts reduction (CAR) is a challenging problem in the field of remote sensing. Most recent deep learning based methods have demonstrated superior performance over the previous hand-crafted methods. In this paper, we propose an end-to-end one-two-one (OTO) network, to combine different deep models, i.e., summation and difference models, to solve the CAR problem. Particularly, the difference model motivated by the Laplacian pyramid is designed to obtain the high frequency information, while the summation model aggregates the low frequency information. We provide an in-depth investigation into our OTO architecture based on the Taylor expansion, which shows that these two kinds of information can be fused in a nonlinear scheme to gain more capacity of handling complicated image compression artifacts, especially the blocking effect in compression. Extensive experiments are conducted to demonstrate the superior performance of the OTO networks, as compared to the state-of-the-arts on remote sensing datasets and other benchmark datasets. The source code will be available here: https://github.com/bczhangbczhang/.

Bibliographic note

s is the author’s version of a work that was accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in ISPRS Journal of Photogrammetry and Remote Sensing, 145, Part A, 2018 DOI: 10.1016/j.isprsjprs.2018.01.003