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Depth Extension for Multiple Focused Images by Adaptive Point Spread Function

机译:自适应点扩展功能扩展多聚焦图像的深度

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摘要

A novel depth fusion algorithm is proposed for multi-focused images based on point spread functions (PSF). In this paper, firstly, a discrete PSF model is present and functions with different parameters are prepared for the proposed algorithm. Based on the analysis of the effect of PSF. the detail process to fuse the depth of multi-focused images is described. By employing PSF to convolve each original images and comparing with its adjacent one in the image sequence, the focused and defocused region in the original images may be located. Combining the the focused region in the images according to Choose Max rules, an all-in-focus image may be got. The complexity of the algorithm for an image series with more than two original images is discussed at the end of this paper. Experimental results show that the image is distinctly segmented into multi-regions and the image edge is legible as well. The proposed algorithm based on PSF convolvetion as a focus measure has been shown to be experimentally valid. The fusion results are satisfactory with smooth transitions across region boundaries.
机译:提出了一种基于点扩展函数(PSF)的多焦点图像深度融合算法。本文首先提出了一种离散的PSF模型,并为该算法准备了具有不同参数的函数。基于PSF的效果分析。描述了融合多焦点图像深度的详细过程。通过使用PSF对每个原始图像进行卷积并与图像序列中的相邻图像进行比较,可以定位原始图像中的聚焦区域和散焦区域。根据“选择最大”规则组合图像中的聚焦区域,可以获得全聚焦图像。本文最后讨论了具有两个以上原始图像的图像序列算法的复杂性。实验结果表明,图像被清晰地分割为多个区域,图像边缘也清晰可见。实验证明,以PSF卷积为重点的算法是有效的。融合结果令人满意,跨区域边界平滑过渡。

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