Semantic edge detection has recently gained a lot of attention as an image-processing task, mainly because of its wide range of real-world applications. This is based on the fact that edges in images contain most of the semantic information. Semantic edge detection involves two tasks, namely pure edge detection and edge classification. Those are in fact fundamentally distinct in terms of the level of abstraction that each task requires. This fact is known as the distracted supervision paradox and limits the possible performance of a supervised model in semantic edge detection. In this work, we will present a novel hybrid method that is based on a combination of the model-based concept of shearlets, which provides probably optimally sparse approximations of a model class of images, and the data-driven method of a suitably designed convolutional neural network. We show that it avoids the distracted supervision paradox and achieves high performance in semantic edge detection. In addition, our approach requires significantly fewer parameters than a pure data-driven approach. Finally, we present several applications such as tomographic reconstruction and show that our approach significantly outperforms former methods, thereby also indicating the value of such hybrid methods for biomedical imaging.

中文翻译：

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Shearlets 作为语义边缘检测的特征提取器：基于模型和数据驱动的领域

语义边缘检测最近作为一种图像处理任务受到了广泛的关注，主要是因为它在现实世界中具有广泛的应用。这是基于图像中的边缘包含大部分语义信息的事实。语义边缘检测涉及两个任务，即纯边缘检测和边缘分类。事实上，就每个任务所需的抽象级别而言，它们是根本不同的。这一事实被称为分散监督悖论，并限制了监督模型在语义边缘检测中的可能性能。在这项工作中，我们将提出一种新颖的混合方法，该方法基于基于模型的剪切波概念的组合，它提供了图像模型类的最佳稀疏近似，以及适当设计的卷积的数据驱动方法神经网络。我们证明它避免了分散监督悖论，并在语义边缘检测中实现了高性能。此外，我们的方法比纯数据驱动的方法需要的参数少得多。最后，我们提出了断层扫描重建等几种应用，并表明我们的方法明显优于以前的方法，从而也表明了这种混合方法对于生物医学成像的价值。