Digital projectors have been increasingly utilized in various commercial and scientific applications. However, they are prone to the out-of-focus blurring problem since their depth-of-fields are typically limited. In this paper, we explore the feasibility of utilizing a deep learning-based approach to analyze the spatially-varying and depth-dependent defocus properties of digital projectors. A multimodal displaying/imaging system is built for capturing images projected at various depths. Based on the constructed dataset containing well-aligned in-focus, out-of-focus, and depth images, we propose a novel multi-channel residual deep network model to learn the end-to-end mapping function between the in-focus and out-of-focus image patches captured at different spatial locations and depths. To the best of our knowledge, it is the first research work revealing that the complex spatially-varying and depth-dependent blurring effects can be accurately learned from a number of real-captured image pairs instead of being hand-crafted as before. Experimental results demonstrate that our proposed deep learning-based method significantly outperforms the state-of-the-art defocus kernel estimation techniques and thus leads to better out-of-focus compensation for extending the dynamic ranges of digital projectors.

中文翻译：

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多通道残差网络模型，用于准确估计空间变化和深度相关的散焦内核。

数字投影仪已被越来越多地用于各种商业和科学应用中。但是，由于它们的景深通常受到限制，因此它们容易出现离焦模糊的问题。在本文中，我们探索了使用基于深度学习的方法来分析数字投影仪的空间变化和深度依赖的散焦特性的可行性。建立了一种多模式显示/成像系统，用于捕获在不同深度投影的图像。基于构造好的数据集，其中包含良好对准的焦点，焦点外和深度图像，我们提出了一种新颖的多通道残差深度网络模型，以学习焦点与焦点之间的端到端映射功能。在不同的空间位置和深度处捕获的散焦图像块。据我们所知，这是第一项研究工作，揭示了可以从许多实际捕获的图像对中准确地学习复杂的随空间变化和与深度相关的模糊效果，而无需像以前那样手工制作。实验结果表明，我们提出的基于深度学习的方法明显优于最新的散焦核估计技术，因此可以为扩展数字投影仪的动态范围提供更好的散焦补偿。