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One-two-one networks for compression artifacts reduction in remote sensing
ISPRS Journal of Photogrammetry and Remote Sensing ( IF 12.7 ) Pub Date : 2018-02-17 , DOI: 10.1016/j.isprsjprs.2018.01.003
Baochang Zhang , Jiaxin Gu , Chen Chen , Jungong Han , Xiangbo Su , Xianbin Cao , Jianzhuang Liu

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/.



中文翻译:

一对一网络减少遥感中的压缩伪影

减少压缩伪像(CAR)是遥感领域中一个具有挑战性的问题。最新的基于深度学习的方法已证明比以前的手工方法具有更好的性能。在本文中,我们提出了一种端到端的一对二(OTO)网络,以结合不同的深度模型(即求和模型和差模型)来解决CAR问题。特别是,设计了由拉普拉斯金字塔驱动的差分模型以获得高频信息,而求和模型则汇总了低频信息。我们对基于泰勒展开式的OTO架构进行了深入研究,结果表明可以将这两种信息以非线性方案进行融合,从而获得更多处理复杂图像压缩伪像的能力,尤其是压缩中的阻塞效果。与遥感数据集和其他基准数据集上的最新技术相比,进行了广泛的实验以证明OTO网络的优越性能。源代码将在此处提供:https://github.com/bczhangbczhang/。

更新日期:2018-06-03
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