Huang Feng, Li Yunxiang, Ye Xiaojing, Wu Jing
School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China.
Sensors (Basel). 2024 Jul 19;24(14):4686. doi: 10.3390/s24144686.
Infrared images hold significant value in applications such as remote sensing and fire safety. However, infrared detectors often face the problem of high hardware costs, which limits their widespread use. Advancements in deep learning have spurred innovative approaches to image super-resolution (SR), but comparatively few efforts have been dedicated to the exploration of infrared images. To address this, we design the Residual Swin Transformer and Average Pooling Block (RSTAB) and propose the SwinAIR, which can effectively extract and fuse the diverse frequency features in infrared images and achieve superior SR reconstruction performance. By further integrating SwinAIR with U-Net, we propose the SwinAIR-GAN for real infrared image SR reconstruction. SwinAIR-GAN extends the degradation space to better simulate the degradation process of real infrared images. Additionally, it incorporates spectral normalization, dropout, and artifact discrimination loss to reduce the potential image artifacts. Qualitative and quantitative evaluations on various datasets confirm the effectiveness of our proposed method in reconstructing realistic textures and details of infrared images.
红外图像在遥感和消防安全等应用中具有重要价值。然而，红外探测器常常面临硬件成本高昂的问题，这限制了它们的广泛应用。深度学习的进步推动了图像超分辨率（SR）的创新方法，但相对较少的工作致力于红外图像的探索。为了解决这个问题，我们设计了残差Swin Transformer和平均池化块（RSTAB），并提出了SwinAIR，它可以有效地提取和融合红外图像中的不同频率特征，并实现卓越的超分辨率重建性能。通过进一步将SwinAIR与U-Net集成，我们提出了用于真实红外图像超分辨率重建的SwinAIR-GAN。SwinAIR-GAN扩展了退化空间，以更好地模拟真实红外图像的退化过程。此外，它还引入了谱归一化、随机失活和伪像判别损失，以减少潜在的图像伪像。在各种数据集上的定性和定量评估证实了我们提出的方法在重建红外图像逼真纹理和细节方面的有效性。
