论文标题
带有通道分裂网络的双边网络和用于热图像超分辨率的变压器
Bilateral Network with Channel Splitting Network and Transformer for Thermal Image Super-Resolution
论文作者
论文摘要
近年来，热图像超分辨率（TISR）问题已成为一个有吸引力的研究主题。 TISR将用于广泛的领域，包括军事，医疗，农业和动物生态学。由于PBVS-2020和PBVS-2021研讨会挑战的成功，TISR的结果不断改善，并吸引了更多的研究人员注册PBVS-2022挑战。在本文中，我们将向PBVS-2022挑战介绍提交的技术细节，该挑战设计具有频道分裂网络和变压器（BN-CSNT）的双边网络，以解决TISR问题。首先，我们设计了一个基于带有变压器的通道分裂网络的上下文分支，以获取足够的上下文信息。其次，我们设计了一个带有浅层变压器的空间分支，以提取可以保留空间信息的低水平特征。最后，对于上下文分支，为了融合通道拆分网络和变压器的功能，我们提出了一个注意力改进模块，然后通过建议的功能融合模块融合了上下文分支和空间分支的特征。所提出的方法可以实现X4的PSNR = 33.64，SSIM = 0.9263，PSNR = 21.08，SSIM = 0.7803，x2在PBVS-2022挑战测试数据集中。
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.
