# Infrared Image Super-Resolution via Lightweight Information Split Network

arXiv: https://arxiv.org/abs/2405.10561 · HTML: https://mlsanity.com/paper/2405.10561/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 35 references that are in the map.
- First posted on arXiv 17 May 2024.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021)
- [Enhanced Deep Residual Networks for Single Image Super-Resolution](https://mlsanity.com/paper/1707.02921/) (2017)
- [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015)
- [Pre-Trained Image Processing Transformer](https://mlsanity.com/paper/2012.00364/) (2020)
- [Lightweight Image Super-Resolution with Information Multi-distillation Network](https://mlsanity.com/paper/1909.11856/) (2019)
- [Residual Dense Network for Image Restoration](https://mlsanity.com/paper/1812.10477/) (2018)
- [Efficient Image Super-Resolution Using Pixel Attention](https://mlsanity.com/paper/2010.01073/) (2020)
- [Blueprint Separable Residual Network for Efficient Image Super-Resolution](https://mlsanity.com/paper/2205.05996/) (2022)
- [The Elements of End-to-end Deep Face Recognition: A Survey of Recent Advances](https://mlsanity.com/paper/2009.13290/) (2020)
- [When Shift Operation Meets Vision Transformer: An Extremely Simple Alternative to Attention Mechanism](https://mlsanity.com/paper/2201.10801/) (2022)
- [Infrared Image Super-Resolution via Heterogeneous Convolutional WGAN](https://mlsanity.com/paper/2109.00960/) (2021)

## Built on by (most cited first, 11 of 11)

- [Time Series Modeling for Heart Rate Prediction: From ARIMA to Transformers](https://mlsanity.com/paper/2406.12199/) (2024)
- [A Lightweight GAN-Based Image Fusion Algorithm for Visible and Infrared Images](https://mlsanity.com/paper/2409.15332/) (2024)
- [Exploration of Multi-Scale Image Fusion Systems in Intelligent Medical Image Analysis](https://mlsanity.com/paper/2406.18548/) (2024)
- [Application of Natural Language Processing in Financial Risk Detection](https://mlsanity.com/paper/2406.09765/) (2024)
- [Deep Learning in Medical Image Classification from MRI-based Brain Tumor Images](https://mlsanity.com/paper/2408.00636/) (2024)
- [Enhancing Deep Learning with Optimized Gradient Descent: Bridging Numerical Methods and Neural Network Training](https://mlsanity.com/paper/2409.04707/) (2024)
- [Real-time Video Target Tracking Algorithm Utilizing Convolutional Neural Networks (CNN)](https://mlsanity.com/paper/2411.18314/) (2024)
- [Multicam-SLAM: Non-overlapping Multi-camera SLAM for Indirect Visual Localization and Navigation](https://mlsanity.com/paper/2406.06374/) (2024)
- [Electroencephalogram Emotion Recognition via AUC Maximization](https://mlsanity.com/paper/2408.08979/) (2024)
- [Synthetic Simplicity: Unveiling Bias in Medical Data Augmentation](https://mlsanity.com/paper/2407.21674/) (2024)
- [TIRMamba: A Thermal-Prior-Modulated State-Space Network for Sub-Million-Parameter Infrared Image Super-Resolution](https://mlsanity.com/paper/2610.05182/) (2026)

## Abstract (arXiv)

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.