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A Computationally Efficient Algorithm for Multi-Focus Image Reconstruction

机译：一种高效的多焦点图像重建算法

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摘要

A method for synthesizing enhanced depth of field digital still camera pictures using multiple differently focused images is presented. This technique exploits only spatial image gradients in the initial decision process. The spatial gradient as a focus measure has been shown to be experimentally valid and theoretically sound under weak assumptions with respect to unimodality and monotonicity. Subsequent majority filtering corroborates decisions with those of neighboring pixels, while the use of soft decisions enables smooth transitions across region boundaries. Furthermore, these last two steps add algorithmic robustness for coping with both sensor noise and optics-related effects, such as misregistration or optical flow, and minor intensity fluctuations. The dependence of these optical effects on several optical parameters is analyzed and potential remedies that can allay their impact with regard to the technique's limitations are discussed. Several examples of image synthesis using the algorithm are presented. Finally, leveraging the increasing functionality and emerging processing capabilities of digital still cameras, the method is shown to entail modest hardware requirements and is implementable using a parallel or general purpose processor.
机译：提出了一种使用多个不同聚焦的图像合成景深增强的数码相机图片的方法。该技术在初始决策过程中仅利用空间图像梯度。在有关单峰和单调性的较弱假设下，已证明空间梯度作为一种焦点措施在实验上是有效的，并且在理论上是合理的。随后的多数滤波可与相邻像素的决策一起确定决策，而使用软决策可实现跨区域边界的平滑过渡。此外，这最后两个步骤增加了算法的鲁棒性，以应对传感器噪声和与光学相关的影响，例如配准误差或光流，以及较小的强度波动。分析了这些光学效应对几个光学参数的依赖性，并讨论了可以减轻其对技术局限性影响的潜在补救措施。给出了使用该算法进行图像合成的几个示例。最后，利用数字静态相机的不断增长的功能和新兴的处理能力，该方法显示出对硬件的要求不高，并且可以使用并行或通用处理器实现。

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