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.

翻译：单图像超分辨率是一项成熟的像素级视觉任务，旨在从降质的低分辨率图像中重建高分辨率图像。尽管利用深度神经网络进行超分已取得显著进展，但现有的大多数深度学习架构层数众多，导致计算复杂度高和内存需求大。在红外图像超分辨率场景中，由于红外设备通常存在严格的存储和计算约束，这些问题尤为突出。为解决这些挑战，我们提出了一种新颖、高效且精确的单幅红外图像超分辨率模型，称为轻量级信息分割网络（LISN）。该模型包含四个主要组件：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。该模型的一个关键创新在于引入了轻量级信息分割块（LISB）用于深层特征提取。LISB通过顺序过程提取层次化特征，然后基于所考虑特征的相关性进行聚合。通过融合通道分割与移位操作，LISB成功地在增强超分辨率性能与保持轻量级框架之间取得了理想平衡。全面的实验评估表明，所提出的LISN在超分辨率质量和模型复杂度方面均优于当前最先进方法，验证了其在资源受限的红外成像应用中实际部署的有效性。