AI 中文总结
该研究针对单图像散焦去模糊评估难的问题，基于RealBokeh数据集构建RealDefocus基准测试，提供配对数据、分割及评估框架，并概述跨数据集验证协议，用于评估重建质量与泛化能力，项目页面公开。
AI 中文摘要
单图像散焦去模糊旨在从单个散焦观测中恢复全聚焦图像，但由于缺乏具有对齐的散焦/清晰对的现实高分辨率数据集和标准化协议，严格且可重复的评估仍然具有挑战性。我们基于RealDefocus构建，它源自最初用于散景渲染的真实世界RealBokeh数据集。RealDefocus提供配对的散焦输入和清晰的地面真值图像、预定义的训练/验证/测试分割以及用于比较图像恢复和神经渲染方法的统一评估框架。我们还概述了一种具有跨数据集验证的基准测试协议，以评估重建质量和泛化能力。项目页面可公开获取。
英文摘要
Single-Image Defocus Deblurring (SIDD) aims to recover an all-in-focus image from a single defocused observation, but rigorous and reproducible evaluation remains challenging due to the scarcity of realistic, high-resolution datasets with well-aligned defocused/sharp pairs and standardized protocols. We build on RealDefocus, a benchmark derived from the real-world RealBokeh dataset originally proposed for Bokeh Rendering. RealDefocus provides paired defocused inputs and sharp ground truth images, predefined training/validation/test splits, and a unified evaluation framework for comparing image restoration and neural rendering approaches. We further outline a benchmarking protocol with cross-dataset validation to assess reconstruction quality and generalization. The project page is publicly available at: www.github.com/TimSeizinger/RealDefocus-Benchmark.
CommentsAccepted at ICIP 2026
