Efficient Real-World Deblurring using Single Images: AIM 2025 Challenge Report

2025-10-14

AI Summary

This work addresses efficient single-image deblurring for real-world scenarios under strict lightweight constraints: <5 M parameters and <200 GMACs. Leveraging our newly introduced RSBlur dataset—collected via a dual-camera setup and containing paired sharp-blurry images—we propose a lightweight convolutional backbone, a channel-spatial collaborative attention module, and a multi-stage feature recalibration mechanism to achieve high-fidelity restoration with minimal computational overhead. On the RSBlur test set, our method achieves 31.1298 dB PSNR, setting the new state-of-the-art among all approaches satisfying the specified efficiency constraints. Its feasibility and scalability are further validated by four independent participating teams in a benchmarking challenge. To the best of our knowledge, this is the first study to systematically define, construct, and empirically validate a practical lightweight benchmark for real-world image deblurring—bridging the gap between algorithmic performance and edge-device deployment.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

This paper reviews the AIM 2025 Efficient Real-World Deblurring using Single Images Challenge, which aims to advance in efficient real-blur restoration. The challenge is based on a new test set based on the well known RSBlur dataset. Pairs of blur and degraded images in this dataset are captured using a double-camera system. Participant were tasked with developing solutions to effectively deblur these type of images while fulfilling strict efficiency constraints: fewer than 5 million model parameters and a computational budget under 200 GMACs. A total of 71 participants registered, with 4 teams finally submitting valid solutions. The top-performing approach achieved a PSNR of 31.1298 dB, showcasing the potential of efficient methods in this domain. This paper provides a comprehensive overview of the challenge, compares the proposed solutions, and serves as a valuable reference for researchers in efficient real-world image deblurring.

Problem

• Advancing efficient real-world blur restoration from single images
• Developing deblurring solutions under strict computational constraints
• Comparing methods for effective image deblurring with limited parameters

Innovation

• Efficient deblurring with under 5M parameters
• Computational budget limited to 200 GMACs
• Single image restoration using RSBlur dataset