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

TL;DR

This paper introduces an end-to-end OTO network that effectively reduces compression artifacts in remote sensing images by combining high and low frequency information through a novel nonlinear fusion scheme.

Contribution

The paper proposes a new OTO network architecture that fuses summation and difference models for improved compression artifacts reduction in remote sensing images.

Findings

Outperforms state-of-the-art methods on remote sensing datasets.

Effectively captures high-frequency details using the difference model.

Enhances low-frequency information aggregation with the summation model.

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.