# SHF: Small: An Integrated Architecture-System Framework for High-Quality and Cost-Efficient Learning-Based Super Resolution

> **NSF 01002526DB NSF RESEARCH & RELATED ACTIVIT** · University of Houston (TX) · $539,998

## Abstract

Super resolution is a technology that enhances the quality of digital images by increasing their resolution, making them appear sharper and more detailed. This capability is important in a wide range of applications, such as medical imaging, satellite analysis, security monitoring, and immersive technologies like virtual reality (VR). However, current super resolution methods—many of which are based on deep learning—can be slow and require large amounts of computing power. This project aims to develop a new generation of learning-based super resolution that delivers high image quality, fast performance, and low power consumption. This project will have a transformative impact across a wide range of fields like medical imaging, entertainment, VR, surveillance, and autonomous vehicles. Moreover, by enabling sharper images with fast, energy-efficient processing, this research allows edge devices, such as drones and mobile phones, to perform real-time image enhancement without cloud reliance, saving bandwidth and power. This will drive more efficient processing in constrained environments, reduce costs for high-quality imaging, and unlock new possibilities for Artificial Intelligence (AI)-driven applications in smart cities and augmented reality. The project will expose more students to computing research through outreach, curriculum development activities, and disseminating research infrastructure for education and training. 

This project develops an integrated framework to e

## Key facts

- **NSF award ID:** 2504152
- **Awardee organization:** University of Houston (TX)
- **SAM.gov UEI:** QKWEF8XLMTT3
- **PI:** Xin Fu
- **Primary program:** 01002526DB NSF RESEARCH & RELATED ACTIVIT
- **All programs:** SMALL PROJECT, COMPUTER ARCHITECTURE
- **Estimated total:** $539,998
- **Funds obligated:** $539,998
- **Transaction type:** Standard Grant
- **Period:** 07/01/2025 → 06/30/2028

## Primary source

NSF Award Search: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2504152

## Citation

> US National Science Foundation, Award 2504152, SHF: Small: An Integrated Architecture-System Framework for High-Quality and Cost-Efficient Learning-Based Super Resolution. Retrieved via AI Analytics 2026-06-08 from https://api.ai-analytics.org/grant/nsf/2504152. Licensed CC0.

---

*[NSF Awards dataset](/datasets/nsf-awards) · CC0 1.0*