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Single-Image Depth From Defocus With Coded Aperture and Diffusion Posterior Sampling
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DOI：10.1109/tci.2026.3697618.png)
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Abstract
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We propose a single-snapshot coded-aperture depth-from-defocus (DFD) method that reconstructs RGBD by posterior-guided diffusion, combining a learned RGBD diffusion prior with a differentiable DFD forward model. Given a measurement, we approximately sample from the posterior induced by the forward model (data fidelity) and the diffusion prior, using a denoise–correct–renoise procedure that applies data-consistency updates in the denoised domain. Unlike U-Net–style regressors, our approach requires no paired defocus–RGBD training data and does not couple training to a specific camera configuration. Experiments on comprehensive simulations and a prototype camera show stable RGBD reconstructions across noise levels, outperforming U-Net baselines and a classical coded-aperture DFD method.Our implementation and experimental configuration will be released publicly upon publication.
Keywords:
DFD
coded aperture
diffusion model
DPS
camera parameter free
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