# The First Challenge on Remote Sensing Infrared Image Super-Resolution at NTIRE 2026: Benchmark Results and Method Overview arXiv: https://arxiv.org/abs/2604.21312 ยท HTML: https://mlsanity.com/paper/2604.21312/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Builds on 37 of its 53 references that are in the map. - First posted on arXiv 23 Apr 2026. ## Builds on (most cited first, 30 of 37) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021) - [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023) - [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016) - [SwinIR: Image Restoration Using Swin Transformer](https://mlsanity.com/paper/2108.10257/) (2021) - [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021) - [Image Super-Resolution Using Very Deep Residual Channel Attention Networks](https://mlsanity.com/paper/1807.02758/) (2018) - [Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network](https://mlsanity.com/paper/1609.05158/) (2016) - [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024) - [Accurate Image Super-Resolution Using Very Deep Convolutional Networks](https://mlsanity.com/paper/1511.04587/) (2015) - [Residual Dense Network for Image Super-Resolution](https://mlsanity.com/paper/1802.08797/) (2018) - [Activating More Pixels in Image Super-Resolution Transformer](https://mlsanity.com/paper/2205.04437/) (2022) - [MambaIR: A Simple Baseline for Image Restoration with State-Space Model](https://mlsanity.com/paper/2402.15648/) (2024) - [SeeSR: Towards Semantics-Aware Real-World Image Super-Resolution](https://mlsanity.com/paper/2311.16518/) (2023) - [DiffIR: Efficient Diffusion Model for Image Restoration](https://mlsanity.com/paper/2303.09472/) (2023) - [Dual Aggregation Transformer for Image Super-Resolution](https://mlsanity.com/paper/2308.03364/) (2023) - [HAT: Hybrid Attention Transformer for Image Restoration](https://mlsanity.com/paper/2309.05239/) (2023) - [Seven ways to improve example-based single image super resolution](https://mlsanity.com/paper/1511.02228/) (2015) - [Progressive Focused Transformer for Single Image Super-Resolution](https://mlsanity.com/paper/2503.20337/) (2025) - [The Eleventh NTIRE 2026 Efficient Super-Resolution Challenge Report](https://mlsanity.com/paper/2604.03198/) (2026) - [NTIRE 2026 Challenge on Robust AI-Generated Image Detection in the Wild](https://mlsanity.com/paper/2604.11487/) (2026) - [NTIRE 2026 Challenge on Bitstream-Corrupted Video Restoration: Methods and Results](https://mlsanity.com/paper/2604.06945/) (2026) - [NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: Multi-Exposure Image Fusion in Dynamic Scenes (Track 2)](https://mlsanity.com/paper/2604.09030/) (2026) - [NTIRE 2026 Challenge on Single Image Reflection Removal in the Wild: Datasets, Results, and Methods](https://mlsanity.com/paper/2604.10321/) (2026) - [NTIRE 2026 Challenge on Short-form UGC Video Restoration in the Wild with Generative Models: Datasets, Methods and Results](https://mlsanity.com/paper/2604.10551/) (2026) - [NTIRE 2026 The Second Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results](https://mlsanity.com/paper/2604.10634/) (2026) - [The Second Challenge on Real-World Face Restoration at NTIRE 2026: Methods and Results](https://mlsanity.com/paper/2604.10532/) (2026) - [The Second Challenge on Cross-Domain Few-Shot Object Detection at NTIRE 2026: Methods and Results](https://mlsanity.com/paper/2604.11998/) (2026) - [NTIRE 2026 The 3rd Restore Any Image Model (RAIM) Challenge: AI Flash Portrait (Track 3)](https://mlsanity.com/paper/2604.11230/) (2026) ## Built on by (most cited first, 7 of 7) - [Robust Deepfake Detection, NTIRE 2026 Challenge: Report](https://mlsanity.com/paper/2604.24163/) (2026) - [The First Controllable Bokeh Rendering Challenge at NTIRE 2026](https://mlsanity.com/paper/2605.05510/) (2026) - [The Third Challenge on Image Denoising at NTIRE 2026: Methods and Results](https://mlsanity.com/paper/2606.16031/) (2026) - [NTIRE 2026 Challenge on Efficient Low Light Image Enhancement: Methods and Results](https://mlsanity.com/paper/2605.02212/) (2026) - [NTIRE 2026 Low-light Enhancement: Twilight Cowboy Challenge](https://mlsanity.com/paper/2608.09782/) (2026) - [Discrete Annotation, Continuous Preference: Rethinking Supervision for Accurate and Generalizable Aesthetic Image Cropping](https://mlsanity.com/paper/2610.00582/) (2026) - [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) This paper presents the NTIRE 2026 Remote Sensing Infrared Image Super-Resolution (x4) Challenge, one of the associated challenges of NTIRE 2026. The challenge aims to recover high-resolution (HR) infrared images from low-resolution (LR) inputs generated through bicubic downsampling with a x4 scaling factor. The objective is to develop effective models or solutions that achieve state-of-the-art performance for infrared image SR in remote sensing scenarios. To reflect the characteristics of infrared data and practical application needs, the challenge adopts a single-track setting. A total of 115 participants registered for the competition, with 13 teams submitting valid entries. This report summarizes the challenge design, dataset, evaluation protocol, main results, and the representative methods of each team. The challenge serves as a benchmark to advance research in infrared image super-resolution and promote the development of effective solutions for real-world remote sensing applications.