In recent years, the Thermal Image Super-Resolution (TISR) problem has become
an attractive research topic. TISR would been used in a wide range of fields,
including military, medical, agricultural and animal ecology. Due to the
success of PBVS-2020 and PBVS-2021 workshop challenge, the result of TISR keeps
improving and attracts more researchers to sign up for PBVS-2022 challenge. In
this paper, we will introduce the technical details of our submission to
PBVS-2022 challenge designing a Bilateral Network with Channel Splitting
Network and Transformer(BN-CSNT) to tackle the TISR problem. Firstly, we
designed a context branch based on channel splitting network with transformer
to obtain sufficient context information. Secondly, we designed a spatial
branch with shallow transformer to extract low level features which can
preserve the spatial information. Finally, for the context branch in order to
fuse the features from channel splitting network and transformer, we proposed
an attention refinement module, and then features from context branch and
spatial branch are fused by proposed feature fusion module. The proposed method
can achieve PSNR=33.64, SSIM=0.9263 for x4 and PSNR=21.08, SSIM=0.7803 for x2
in the PBVS-2022 challenge test dataset.

本文介绍了一个名为 BN-CSNT 的双边网络设计，通过频道分裂网络和 Transformer 解决了 TISR 问题，最终在 PBVS-2022 挑战测试数据集上实现了良好的表现，x4 的 PSNR 为 33.64，SSIM 为 0.9263，x2 的 PSNR 为 21.08，SSIM 为 0.7803。