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首页> 外文会议>IEEE International Conference on Image Processing >Detecting Curves in Very Noisy Images Using Fourier-Argand Moments
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Detecting Curves in Very Noisy Images Using Fourier-Argand Moments

机译：使用傅里叶-阿甘德矩检测非常嘈杂的图像中的曲线

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Detection of curves (e.g., ridges) from very noisy images is an important yet challenging task in photon-starved imaging applications (e.g., nuclear imaging modalities, fluorescence/electron microscopy, radioastronomy, first photon/light-in-flight imaging).In this paper, we exploit the consistency of the image along the curve, i.e., the fact that the image changes slowly when we move along the curve—"locally" laminar images. We compute a sequence of complex scalars that we call Fourier-Argand moments, and show that the direction of variation of a laminar image is purely encoded in the phase of these moments. In particular, focusing on ridges located at the center of the image, we show that using these moments altogether in a frequency estimation algorithm provides a very accurate and highly robust estimate of the direction of the ridge: we demonstrate this accuracy for noise levels as high as −10dB.We then show how to detect curves—i.e., local ridges—by computing the Fourier Argand moments within a sliding window across the image, and design a consistency map whose thresholding allows to keep only the pixels on the curve.Numerical experiments on both synthetic images and real images (low light photography) demonstrate the accuracy and robustness to noise of the proposed method, compared to a state of the art method.
机译：在光子匮乏的成像应用（例如核成像模式，荧光/电子显微镜，放射天文学，首次光子/飞行中成像）中，从非常嘈杂的图像中检测曲线（例如脊）是一项重要但具有挑战性的任务。在本文中，我们利用图像沿着曲线的一致性，即当我们沿着曲线移动时图像变化缓慢的事实-“局部”层状图像。我们计算了一个称为傅里叶-阿甘德矩的复杂标量序列，并表明层流图像变化方向完全是在这些矩的相位中编码的。特别是，着眼于位于图像中心的山脊，我们表明在频率估计算法中完全使用这些矩可以对山脊的方向提供非常准确且高度鲁棒的估计：我们证明了这种噪声水平的准确性很高为-10dB。然后，我们展示了如何通过计算图像滑动窗口内的傅立叶Argand矩来检测曲线（即局部脊），并设计一个一致性图，其阈值允许仅将像素保留在曲线上。数值实验与现有技术相比，在合成图像和真实图像（弱光摄影）上的演示都证明了所提出方法的准确性和对噪声的鲁棒性。

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