Video Magnification in the Wild Using Fractional Anisotropy in Temporal Distribution

Shoichiro Takeda, Yasunori Akagi, Kazuki Okami, M. Isogai, H. Kimata
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引用次数: 17

Abstract

Video magnification methods can magnify and reveal subtle changes invisible to the naked eye. However, in such subtle changes, meaningful ones caused by physical and natural phenomena are mixed with non-meaningful ones caused by photographic noise. Therefore, current methods often produce noisy and misleading magnification outputs due to the non-meaningful subtle changes. For detecting only meaningful subtle changes, several methods have been proposed but require human manipulations, additional resources, or input video scene limitations. In this paper, we present a novel method using fractional anisotropy (FA) to detect only meaningful subtle changes without the aforementioned requirements. FA has been used in neuroscience to evaluate anisotropic diffusion of water molecules in the body. On the basis of our observation that temporal distribution of meaningful subtle changes more clearly indicates anisotropic diffusion than that of non-meaningful ones, we used FA to design a fractional anisotropic filter that passes only meaningful subtle changes. Using the filter enables our method to obtain better and more impressive magnification results than those obtained with state-of-the-art methods.
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在时间分布中使用分数各向异性的野生视频放大
视频放大方法可以放大和显示肉眼看不见的细微变化。然而,在这种微妙的变化中,物理和自然现象所引起的有意义的变化与摄影噪声所引起的无意义的变化混合在一起。因此,由于无意义的细微变化,目前的方法经常产生嘈杂和误导性的放大输出。为了仅检测有意义的细微变化,已经提出了几种方法,但需要人工操作,额外的资源或输入视频场景的限制。在本文中,我们提出了一种新的方法,利用分数各向异性(FA)来检测有意义的细微变化,而不需要上述要求。FA已在神经科学中用于评价水分子在体内的各向异性扩散。在我们观察到有意义细微变化的时间分布比无意义细微变化的时间分布更清楚地表明各向异性扩散的基础上,我们使用FA设计了一个分数型各向异性滤波器,该滤波器只通过有意义的细微变化。使用过滤器使我们的方法获得比使用最先进的方法获得的更好和更令人印象深刻的放大结果。
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