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• The proposed focal stack augmentation enables SFF methods to estimate depth using a reduced stack of just two images without sacrificing precision.method 90%
• The augmentation enriches the stack with an all-in-focus (AiF) image estimated from two input images.technical 90%
• The augmentation includes Energy-of-Difference (EOD) maps computed as the energy of differences between the AiF and input images.technical 90%
• The proposed deep network computes a deep focus volume from augmented focal stacks and iteratively refines depth using convolutional Gated Recurrent Units (ConvGRUs) at multiple scales.method 85%
• Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed augmentation benefits existing state-of-the-art SFF models.result 80%
• The approach maintains state-of-the-art performance with a minimal stack size.result 80%
• A key limitation of existing SFF methods is their reliance on densely sampled, large focal stacks, which limits their practical applicability.limitation 90%
• The proposed augmentation enables existing SFF models to achieve comparable accuracy to methods using larger focal stacks.result 75%

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