Efficient Depth- and Spatially-Varying Image Simulation for Defocus Deblur

2025-06-30

AI Summary

Fixed-focus, large-aperture cameras (e.g., smart glasses) suffer from depth-dependent defocus blur and spatially varying optical aberrations due to shallow depth of field; existing deblurring models trained on open-source datasets exhibit significant domain shift and poor generalization to such real-world imaging conditions. Method: We propose a physics-driven synthetic image generation framework that jointly models depth-dependent defocus blur and spatially varying aberrations for the first time—enabling simulation-to-real domain alignment without fine-tuning on real data. A lightweight low-resolution synthetic dataset is used to train an end-to-end deep network capable of restoring high-resolution (12 MP) real images. Contribution/Results: Our method achieves state-of-the-art performance across diverse challenging scenarios, demonstrating superior efficiency, scalability, and practical deployability compared to prior approaches.

Technology Category

• Computer Vision: Computational Photography, Image & Video Synthesis
• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Code Generation / Program Synthesis from Natural Language

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Modern cameras with large apertures often suffer from a shallow depth of field, resulting in blurry images of objects outside the focal plane. This limitation is particularly problematic for fixed-focus cameras, such as those used in smart glasses, where adding autofocus mechanisms is challenging due to form factor and power constraints. Due to unmatched optical aberrations and defocus properties unique to each camera system, deep learning models trained on existing open-source datasets often face domain gaps and do not perform well in real-world settings. In this paper, we propose an efficient and scalable dataset synthesis approach that does not rely on fine-tuning with real-world data. Our method simultaneously models depth-dependent defocus and spatially varying optical aberrations, addressing both computational complexity and the scarcity of high-quality RGB-D datasets. Experimental results demonstrate that a network trained on our low resolution synthetic images generalizes effectively to high resolution (12MP) real-world images across diverse scenes.

Problem

• Simulating depth-varying defocus for blurry images
• Addressing domain gaps in deep learning models
• Overcoming scarcity of high-quality RGB-D datasets

Innovation

• Efficient dataset synthesis without real-world fine-tuning
• Models depth-dependent defocus and optical aberrations
• Generalizes from low-res synthetic to high-res real images