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 method utilizes camera lens models and parameters to recover depth information from blurred images and has been proven to perform well. However%2C these methods rely on All-In-Focus (AIF) images for depth estimation%2C which is nearly impossible to obtain in real-world applications. To address this issue%2C we propose a self-supervised framework based on 3D Gaussian splatting and Siamese networks. By learning the blur levels at different focal distances of the same scene in the focal stack%2C the framework predicts the defocus map and Circle of Confusion (CoC) from a single defocused image%2C using the defocus map as input to DepthNet for monocular depth estimation. The 3D Gaussian splatting model renders defocused images using the predicted CoC%2C and the differences between these and the real defocused images provide additional supervision signals for the Siamese Defocus self-supervised network. This framework has been validated on both artificially synthesized and real blurred datasets. Subsequent quantitative and visualization experiments demonstrate that our proposed framework is highly effective as a DFD method.?quality=80&w=800)
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