Defocus blur often degrades the performance of image understanding, such as object recognition and image segmentation. Restoring an all-in-focus image from its defocused version is highly beneficial to visual information processing and many photographic applications, despite being a severely ill-posed problem. We propose a novel convolutional neural network architecture AIFNet for removing spatially-varying defocus blur from a single defocused image. We leverage light field synthetic aperture and refocusing techniques to generate a large set of realistic defocused and all-in-focus image pairs depicting a variety of natural scenes for network training. AIFNet consists of three modules: defocus map estimation, deblurring and domain adaptation. The effects and performance of various network components are extensively evaluated. We also compare our method with existing solutions using several publicly available datasets. Quantitative and qualitative evaluations demonstrate that AIFNet shows the state-of-the-art performance.

中文翻译：

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AIFNet：使用基于光场的数据集的全焦点图像恢复网络


散焦模糊通常会降低图像理解的性能，例如对象识别和图像分割。从散焦版本恢复全焦点图像对于视觉信息处理和许多摄影应用非常有益，尽管这是一个严重的不适定问题。我们提出了一种新颖的卷积神经网络架构 AIFNet，用于从单个散焦图像中消除空间变化的散焦模糊。我们利用光场合成孔径和重聚焦技术来生成大量逼真的散焦和全焦点图像对，描绘各种自然场景以进行网络训练。 AIFNet 由三个模块组成：散焦图估计、去模糊和域适应。各种网络组件的效果和性能得到了广泛的评估。我们还使用几个公开可用的数据集将我们的方法与现有解决方案进行比较。定量和定性评估表明 AIFNet 显示了最先进的性能。