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3D shape from focus using LULU operators and discrete pulse transform in the presence of noise

机译：使用LULU运算符从焦点获得3D形状并在存在噪声的情况下进行离散脉冲变换

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摘要

3D shape recovery is an interesting and challenging area of research. Recovering the depth information of an object from a sequence of 2D images with varying focus is known as shape from focus. Focus value of an image carries information about the object and shape from focus is a method which depends on different focused value images. It reconstructs the shape/surface/depth of an object based on the different focused values of the object. These different focused valued images should be captured from the same angle. Calculating the shape of the object from different images with different focused values can be done by applying sharpness detection methods to maximize and detect the focused values. In this paper, we propose new 3D shape recovery techniques based on LULU operators and discrete pulse transform. LULU operators are nonlinear rank selector operators that are efficient with low complexity. They hold consistent separation, total variation and shape preservation properties. Discrete pulse transform is a transform that decomposes image into pulses. Therefore selection of right pulses, give sharpest focus values. The proposed techniques provide better result than traditional techniques in a noisy environment.
机译：3D形状恢复是一个有趣且具有挑战性的研究领域。从具有不同焦点的2D图像序列中恢复对象的深度信息被称为“从焦点形状”。图像的焦点值从焦点携带有关对象和形状的信息，这是一种依赖于不同焦点值图像的方法。它根据对象的不同聚焦值来重建对象的形状/表面/深度。这些不同的聚焦值图像应从相同角度捕获。通过应用锐度检测方法来最大化和检测聚焦值，可以从具有不同聚焦值的不同图像计算对象的形状。在本文中，我们提出了基于LULU算子和离散脉冲变换的3D形状恢复新技术。 LULU运算符是非线性排序选择器运算符，有效且低复杂度。它们具有一致的分离，总体变化和形状保持特性。离散脉冲变换是将图像分解为脉冲的变换。因此，选择正确的脉冲可提供最清晰的聚焦值。所提出的技术在嘈杂的环境中提供了比传统技术更好的结果。

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