We propose a single-snapshot depth-from-defocus (DFD) reconstruction method for coded-aperture imaging that replaces hand-crafted priors with a learned diffusion prior used purely as regularization. Our optimization framework enforces measurement consistency via a differentiable forward model while guiding solutions with the diffusion prior in the denoised image domain, yielding higher accuracy and stability than classical optimization. Unlike U-Net-style regressors, our approach requires no paired defocus-RGBD training data and does not tie training to a specific camera configuration. Experiments on comprehensive simulations and a prototype camera demonstrate consistently strong RGBD reconstructions across noise levels, outperforming both U-Net baselines and a classical coded-aperture DFD method.
@article{arxiv.2509.17427,
title = {Single-Image Depth from Defocus with Coded Aperture and Diffusion Posterior Sampling},
author = {Hodaka Kawachi and Jose Reinaldo Cunha Santos A. V. Silva Neto and Yasushi Yagi and Hajime Nagahara and Tomoya Nakamura},
journal= {arXiv preprint arXiv:2509.17427},
year = {2025}
}