用于牙根成像的超分辨率
Super Resolution for Root Imaging
摘要：
高分辨率相机已经变得非常有助于植物表型分析，提供了一种机制，如目标与背景的歧视，并测量和分析精细的地上植物属性的任务。然而，植物根系的高分辨率（HR）图像的获取比地上数据汇集更具挑战性。因此，需要一种有效的超分辨率（SR）算法来克服传感器的分辨率限制，减少存储空间需求，并提高后续分析（如自动分割）的性能。我们提出了一个SR框架，通过使用卷积神经网络（CNN）增强图像的植物根系。我们比较了三种训练SR模型的方法：i）使用非植物根图像进行训练，ii）使用植物根图像进行训练，iii）使用非植物根图像对模型进行预训练，并使用植物根图像进行微调。我们在一组公开可用的数据集上证明，SR模型即使在使用非根数据集进行训练时也优于基本的双三次插值。此外，我们的分割实验表明，在这项任务上的高性能可以独立于SNR实现。因此，我们得出结论，图像增强的质量取决于应用。
英文摘要：
High-resolution cameras have become very helpful for plant phenotyping by providing a mechanism for tasks such as target versus background discrimination, and the measurement and analysis of fine-above-ground plant attributes. However, the acquisition of high-resolution (HR) imagery of plant roots is more challenging than above-ground data collection. Thus, an effective super-resolution (SR) algorithm is desired for overcoming resolution limitations of sensors, reducing storage space requirements, and boosting the performance of later analysis, such as automatic segmentation. We propose a SR framework for enhancing images of plant roots by using convolutional neural networks (CNNs). We compare three alternatives for training the SR model: i) training with non-plant-root images, ii) training with plant-root images, and iii) pretraining the model with non-plant-root images and fine-tuning with plant-root images. We demonstrate on a collection of publicly available datasets that the SR models outperform the basic bicubic interpolation even when trained with non-root datasets. Also, our segmentation experiments show that high performance on this task can be achieved independently of the SNR. Therefore, we conclude that the quality of the image enhancement depends on the application.
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