CLDefocus: Physically Grounded Compound-Lens Defocus Blur Synthesis

Defocus blur degrades fine image structures and limits vi-sual perception, which can adversely affect downstream vision tasks. Al-though recent deep learning deblurring methods have achieved strongperformance, their effectiveness depends on training data and often de-grades across cameras and lenses due to limited optical diversity and re-alism in existing datasets. In this paper, we propose a pipeline for synthe-sizing realistic defocus deblurring datasets for diverse compound lenses.It integrates efficient wave-optics PSF computation via Debye CZT prop-agation, depth-aware defocus rendering with occlusion handling, and blursynthesis in the radiometrically linear space with camera ISP simulation.This unified pipeline enables the scalable generation of photorealisticdefocus 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 biasfull-reference evaluation. Extensive experiments demonstrate that mod-els trained on CLDefocus achieve improved cross-device generalizationcompared to models trained on existing real and synthetic datasets. Codeand dataset are available at: https://github.com/lykelee/CLDefocus.