One-Two-One Networks for Compression Artifacts Reduction in Remote Sensing
This work addresses compression artifacts, particularly blocking effects, for remote sensing applications, representing an incremental improvement over existing deep learning methods.
The paper tackles compression artifacts reduction in remote sensing by proposing an end-to-end one-two-one network that combines summation and difference models to fuse high and low frequency information, achieving superior performance compared to state-of-the-art methods on remote sensing and benchmark datasets.
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.