Single image super-resolution (SR) is an established pixel-level vision task
aimed at reconstructing a high-resolution image from its degraded
low-resolution counterpart. Despite the notable advancements achieved by
leveraging deep neural networks for SR, most existing deep learning
architectures feature an extensive number of layers, leading to high
computational complexity and substantial memory demands. These issues become
particularly pronounced in the context of infrared image SR, where infrared
devices often have stringent storage and computational constraints. To mitigate
these challenges, we introduce a novel, efficient, and precise single infrared
image SR model, termed the Lightweight Information Split Network (LISN). The
LISN comprises four main components: shallow feature extraction, deep feature
extraction, dense feature fusion, and high-resolution infrared image
reconstruction. A key innovation within this model is the introduction of the
Lightweight Information Split Block (LISB) for deep feature extraction. The
LISB employs a sequential process to extract hierarchical features, which are
then aggregated based on the relevance of the features under consideration. By
integrating channel splitting and shift operations, the LISB successfully
strikes an optimal balance between enhanced SR performance and a lightweight
framework. Comprehensive experimental evaluations reveal that the proposed LISN
achieves superior performance over contemporary state-of-the-art methods in
terms of both SR quality and model complexity, affirming its efficacy for
practical deployment in resource-constrained infrared imaging applications.

提出了一种高效、精确的单像素红外图像超分辨率模型，名为轻量级信息分离网络（LISN），通过使用浅层特征提取、深层特征提取、密集特征融合和高分辨率红外图像重建等组件，以及轻量级信息分离块（LISB）的创新引入，成功地在提高超分辨率性能和轻量级框架之间取得了最佳平衡，优于现有方法，并证实了在资源受限的红外成像应用中的实际部署的有效性。