Fast Projection Defocus Correction for Multiple Projection Surface Types
Fast Projection Defocus Correction for Multiple Projection Surface Types
复制标题
针对多种投影表面类型的快速投影散焦校正
DOI：
10.1109/tii.2020.3003110
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发表时间：
2021-05
影响因子：
12.3
通讯作者：
Jianrong Tan
中科院分区：
文献类型：
--
作者：
Zaixing He;Peilong Li;XY Zhao;Shuyou Zhang;Jianrong Tan
数字投影仪技术的一个主要障碍是投影到具有大深度变化的非理想表面上的图像很容易变得模糊。针对投影曲面形状复杂、深度变化大的特点，提出了一种克服投影散焦的方法。与传统方法相比，该方法具有两个主要优点。首先，提出了一种基于边缘增强的散焦补偿算法，在模糊发生之前对输入图像进行处理以补偿其投影散焦。与以往耗时的补偿算法不同，该算法具有非迭代和开环的特点，具有很高的效率。其次，提出了一种基于正弦投影的估计方法，以减少复杂表面类型上的核估计误差。与以前的方法限于特定的表面类型，所提出的方法可以提供一致的良好的核估计结果，即使是不连续和纹理（非纯白色）投影表面。因此，所提出的方法可以应用到更广泛的适用表面。这两个贡献是通过大量的实验证明的，并与最先进的方法进行了比较。11本文的MATALB代码和实验数据可以在www.example.com上获得。
A major obstacle in digital projector technology is that images projected onto nonideal surfaces with large depth variances can easily become blurred. In this article, present a method to overcome projection defocus for projection surfaces that inevitably have complex shapes and large depth variances. The proposed method has two main advantages over traditional methods. First, an edge-intensification-based defocus compensation algorithm is proposed to manipulate the input image to compensate for its projection defocus before blurring occurs. Unlike previous time-consuming compensation algorithms, the proposed algorithm has very high efficiency, as it is noniterative and open loop. Second, a sinusoidal-projection-based estimation method is proposed to reduce kernel estimation errors on complex surface types. Unlike previous methods limited to specific surface types, the proposed method can provide consistently good kernel estimation results even for discontinuous and textured (nonpure white) projection surfaces. Hence, the proposed method can be applied to a wider range of applicable surfaces. These two contributions are demonstrated through extensive experiments and compared with the state-of-the-art methods.11The MATALB code and experimental data for this article are available at https://github.com/lpl-code/FastProjDefocusComp.
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DOI：
10.1109/tvcg.2018.2868570
发表时间：
2018-09
影响因子：
5.2
作者：
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通讯作者：
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影响因子：
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影响因子：
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作者：
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DOI：
10.1145/1179352.1141974
发表时间：
2006-07
期刊：
ACM SIGGRAPH 2006 Papers
影响因子：
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作者：
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通讯作者：
Li Zhang;S. Nayar
DOI：
10.1109/cvpr.2006.145
发表时间：
2006-06
期刊：
2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06)
影响因子：
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作者：
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通讯作者：
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