Realistic Compound-Lens Defocus Blur Synthesis

2026-07-07

AI Summary

Existing defocus deblurring datasets struggle to support model generalization across diverse cameras and lenses due to limited optical diversity and insufficient realism. To address this, this work proposes the first scalable synthetic defocus dataset generation framework that accommodates heterogeneous compound lenses, occlusion handling, and realistic imaging pipelines. Operating in radiometrically linear space, the framework integrates Debye CZT wave-optics point spread functions, depth-aware blur rendering, and camera ISP simulation to produce the high-fidelity synthetic dataset CLDefocus. Experiments demonstrate that models trained on CLDefocus significantly outperform current methods in cross-device defocus deblurring tasks. Furthermore, the study reveals a systematic bias in full-reference evaluations when using real-captured data, underscoring the advantages of the proposed physically grounded synthesis approach.

Abstract

Defocus blur degrades fine image structures and limits visual perception, which can adversely affect downstream vision tasks. Although recent deep learning deblurring methods have achieved strong performance, their effectiveness depends on training data and often degrades across cameras and lenses due to limited optical diversity and realism in existing datasets. In this paper, we propose a pipeline for synthesizing realistic defocus deblurring datasets for diverse compound lenses. It integrates efficient wave-optics PSF computation via Debye CZT propagation, depth-aware defocus rendering with occlusion handling, and blur synthesis in the radiometrically linear space with camera ISP simulation. This unified pipeline enables the scalable generation of photorealistic defocus datasets with diverse lens characteristics. Using our pipeline, we generate CLDefocus, a large-scale synthetic dataset containing lens-diverse defocus image pairs. We further analyze the limitations of real-captured defocus datasets and show that such imperfections can bias full-reference evaluation. Extensive experiments demonstrate that models trained on CLDefocus achieve improved cross-device generalization compared to models trained on existing real and synthetic datasets.

Problem

• defocus blur
• dataset realism
• optical diversity
• cross-device generalization
• deblurring

Innovation

• defocus blur synthesis
• wave-optics PSF
• Debye CZT propagation
• camera ISP simulation
• cross-device generalization