Infrared Image Super-Resolution via Lightweight Information Split Network
<|reference_start|>Infrared Image Super-Resolution via Lightweight Information Split Network: 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.<|reference_end|>
@article{liu2024infrared,
    title={Infrared Image Super-Resolution via Lightweight Information Split
  Network},
    author={Shijie Liu, Kang Yan, Feiwei Qin, Changmiao Wang, Ruiquan Ge, Kai
  Zhang, Jie Huang, Yong Peng, Jin Cao},
    journal={arXiv preprint arXiv:2405.10561},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10561},
    primaryClass={eess.IV cs.CV}
}