One-Two-One Networks For Compression Artifacts Reduction In Remote Sensing

Keywords

Compression artifacts reduction; Deep learning; One-two-one network; Remote sensing

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/.

Publication Date

11-1-2018

Publication Title

ISPRS Journal of Photogrammetry and Remote Sensing

Volume

145

Number of Pages

184-196

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.isprsjprs.2018.01.003

Socpus ID

85042096911 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85042096911

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