Content-Aware Enhancement of Images With Filamentous Structures
Content-Aware Enhancement of Images With Filamentous Structures
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DOI：
10.1109/tip.2019.2897289
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发表时间：
2019-07-01
影响因子：
10.6
通讯作者：
Weller, Daniel S.
中科院分区：
文献类型：
--
作者：
Jeelani, Haris;Liang, Haoyi;Weller, Daniel S.
在本文中，我们描述了一种新的包含丝状结构的图像增强方法。我们的方法结合了梯度稀疏约束和丝状结构约束，有效地去除了背景中的杂波和噪声。该方法在三类数据上进行了应用和评价：1)神经元的共焦显微镜图像；2)钙成像数据；3)路面图像。我们发现，我们的方法增强后的图像既保留了原始对象的结构细节，又保留了原始对象的强度细节。在神经元显微镜的情况下，我们发现我们的方法增强的神经元与原始结构强度的相关性比众所周知的血管增强方法增强的神经元更好。在模拟的钙离子成像数据上的实验表明，检测到的神经元数目和导出的钙活性的准确性都得到了提高。将我们的方法应用于真实的钙数据，在全视野内发现了更多的显示钙活性的区域。在路面裂缝检测中，使用我们的增强方法后，检测到了较小或较温和的裂缝。
In this paper, we describe a novel enhancement method for images containing filamentous structures. Our method combines a gradient sparsity constraint with a filamentous structure constraint for the effective removal of clutter and noise from the background. The method is applied and evaluated on three types of data: 1) confocal microscopy images of neurons; 2) calcium imaging data; and 3) images of road pavement. We found that the images enhanced by our method preserve both the structure and the intensity details of the original object. In the case of neuron microscopy, we find that the neurons enhanced by our method are better correlated with the original structure intensities than the neurons enhanced by well-known vessel enhancement methods. Experiments on simulated calcium imaging data indicate that both the number of detected neurons and the accuracy of the derived calcium activity are improved. Applying our method to real calcium data, more regions exhibiting calcium activity in the full field of view were found. In road pavement crack detection, smaller or milder cracks were detected after using our enhancement method.