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3-D Shape Recovery from Image Focus Using Gabor Features

机译：使用Gabor功能从图像焦点恢复3D形状

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摘要

Recovering an accurate and precise depth map from a set of acquired 2-D image dataset of the target object each having different focus information is an ultimate goal of 3-D shape recovery. Focus measure algorithm plays an important role in this architecture as it converts the corresponding color value information into focus information which will be then utilized for recovering depth map. This article introduces Gabor features as focus measure approach for recovering depth map from a set of 2-D images. Frequency and orientation representation of Gabor filter features is similar to human visual system and normally applied for texture representation. Due to its little computational complexity, sharp focus measure curve, robust to random noise sources and accuracy, it is considered as superior alternative to most of recently proposed 3-D shape recovery approaches. This algorithm is deeply investigated on real image sequences and synthetic image dataset. The efficiency of the proposed scheme is also compared with the state of art 3-D shape recovery approaches. Finally, by means of two global statistical measures, root mean square error and correlation, we claim that this approach -in spite of simplicity- generates accurate results.
机译：从每个具有不同焦点信息的目标对象的一组采集的2D图像数据集中恢复准确而精确的深度图是3D形状恢复的最终目标。聚焦测量算法在该体系结构中起着重要作用，因为它将相应的颜色值信息转换为聚焦信息，然后将其用于恢复深度图。本文介绍了Gabor功能，这是一种用于从一组二维图像中恢复深度图的焦点测量方法。 Gabor滤镜特征的频率和方向表示类似于人类的视觉系统，通常用于纹理表示。由于其计算复杂度低，焦点测量曲线清晰，对随机噪声源的鲁棒性和准确性，它被认为是大多数最近提出的3-D形状恢复方法的替代方案。该算法在真实图像序列和合成图像数据集上进行了深入研究。提议的方案的效率也与现有技术的3-D形状恢复方法进行了比较。最后，通过两种全局统计量，均方根误差和相关性，我们声称尽管简单，但这种方法仍能产生准确的结果。

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