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首页> 外文期刊>Journal of mathematical imaging and vision >Spatio-Temporal Resolution Enhancement of Vocal Tract MRI Sequences-A Comparison Among Wiener Filter Based Methods
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Spatio-Temporal Resolution Enhancement of Vocal Tract MRI Sequences-A Comparison Among Wiener Filter Based Methods

机译:语音轨迹MRI序列的时空分辨率增强-基于维纳滤波器的方法之间的比较

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摘要

Articulatory Synthesis consists in reproducing speech by means of models of the vocal tract and of articulatory processes. Recent advances in Magnetic Resonance Imaging (MRI) allowed for important improvements with respect to the speech comprehension and the forms taken by the vocal tract. However, one of the main challenges in the field is the fast and at the same time high-quality acquisition of image sequences. Since adopting more powerful acquisition devices might be financially inviable, we propose a method for the spatio-temporal resolution enhancement of the obtained sequences using only digital image processing techniques. The approach involves two stages: (1) the temporal resolution enhancement by means of a motion compensated interpolation technique; and (2) the spatial resolution enhancement by means of a super resolution image reconstruction technique. Considering the spatial resolution enhancement, inspired by two methods available in the literature, three adaptations of theWiener filter were proposed: the statistical interpolation, the multi-temporal approach, and the adaptive Wiener filter. In all cases, a separableMarkovian model and an isotropicmodel were compared for the characterization of the spatial correlation structures. Considering allWiener filter-based approaches, the adaptive Wiener filter outperformed all other approaches.
机译:发音合成包括通过声道模型和发音过程来再现语音。磁共振成像(MRI)的最新进展允许对语音理解和声道的形式进行重要改进。然而,该领域的主要挑战之一是快速且同时高质量地获取图像序列。由于采用功能更强大的采集设备可能在财务上不可行,因此我们提出了一种仅使用数字图像处理技术来增强所获得序列的时空分辨率的方法。该方法涉及两个阶段:(1)通过运动补偿插值技术提高时间分辨率; (2)借助超分辨率图像重建技术提高空间分辨率。考虑到空间分辨率的提高,受文献中可用的两种方法的启发,提出了Wiener滤波器的三种改编方案:统计插值,多时相方法和自适应Wiener滤波器。在所有情况下,都比较了可分离的马尔可夫模型和各向同性模型来表征空间相关结构。考虑到所有基于Wiener滤波器的方法,自适应Wiener滤波器的性能优于所有其他方法。

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