2/18/2026 | USA | technology | ✓ Verified - arxiv.org

RPT-SR: Regional Prior attention Transformer for infrared image Super-Resolution

#infrared super‑resolution #Vision Transformer #spatial priors #regional prior attention #transformer architecture #fixed viewpoint #surveillance imaging #autonomous‑driving SR

📌 Key Takeaways

Vision Transformers excel in general super‑resolution tasks but stagnate in infrared imaging from stationary viewpoints.
The inefficiency arises because current models ignore robust spatial priors that persist across captured frames.
RPT‑SR is proposed to embed these priors within a Transformer architecture to improve infrared SR.
The approach aims to lower redundant computation and enhance reconstruction quality for surveillance and autonomous‑driving applications.

📖 Full Retelling

This paper introduces RPT‑SR, a Regional Prior Attention Transformer designed for infrared image super‑resolution, and it was shared on arXiv (ID 2602.15490v1) in February 2026. The study targets the practical scenario of infrared imaging from fixed or nearly‑static viewpoints—such as those in surveillance systems and autonomous‑driving setups—where general‑purpose Vision Transformer (ViT) models falter by not exploiting the strong, persistent spatial priors characteristic of these scenes. By addressing this shortfall, the work seeks to reduce redundant learning and achieve sub‑optimal performance.

🏷️ Themes

Infrared imaging, Super‑resolution, Vision Transformers, Spatial priors, Fixed‑viewpoint scenarios, Surveillance, Autonomous driving

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Deep Analysis

Why It Matters

The new RPT-SR model targets infrared imaging used in surveillance and autonomous driving, where cameras often view the same scene from a fixed position. By exploiting spatial priors, it reduces redundant learning and improves super-resolution quality, which can enhance object detection and safety.

Context & Background

Vision Transformers dominate super-resolution but are inefficient for static infrared scenes
Infrared cameras capture persistent spatial patterns that can be leveraged
Current models ignore these priors, leading to suboptimal performance

What Happens Next

Researchers will test RPT-SR on real-world infrared datasets and integrate it into edge devices for faster, higher-quality image reconstruction. The approach may inspire new transformer designs that incorporate scene priors for other imaging tasks.

Frequently Asked Questions

What is RPT-SR?

A regional prior attention transformer designed to improve infrared image super-resolution by using spatial priors

How does it differ from existing models?

It focuses on fixed viewpoints and uses regional priors to reduce redundant learning, unlike generic vision transformers

Is the code available?

The authors plan to release the implementation on arXiv, but it is not yet publicly available

Original Source

              arXiv:2602.15490v1 Announce Type: cross 
Abstract: General-purpose super-resolution models, particularly Vision Transformers, have achieved remarkable success but exhibit fundamental inefficiencies in common infrared imaging scenarios like surveillance and autonomous driving, which operate from fixed or nearly-static viewpoints. These models fail to exploit the strong, persistent spatial priors inherent in such scenes, leading to redundant learning and suboptimal performance. To address this, we
            

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Source

arxiv.org