  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.
