论文标题
深层级联的U-NET用于多任务图像处理
Deeply Cascaded U-Net for Multi-Task Image Processing
论文作者
论文摘要
在目前的实践中，许多图像处理任务是顺序完成的（例如，denoising，dehazing，然后进行语义分割）。在本文中，我们提出了一种新型的多任务神经网络体系结构，旨在结合顺序图像处理任务。我们通过为每个单独任务的其他解码途径扩展了U-NET，并探索了从一个途径到另一种途径的输出和连接的深层级联。我们证明了所提出的方法对降解和语义分割以及渐进性的粗到精细语义分割的有效性，并比多个个人或共同训练的网络获得更好的性能，具有较低的可训练参数。
In current practice, many image processing tasks are done sequentially (e.g. denoising, dehazing, followed by semantic segmentation). In this paper, we propose a novel multi-task neural network architecture designed for combining sequential image processing tasks. We extend U-Net by additional decoding pathways for each individual task, and explore deep cascading of outputs and connectivity from one pathway to another. We demonstrate effectiveness of the proposed approach on denoising and semantic segmentation, as well as on progressive coarse-to-fine semantic segmentation, and achieve better performance than multiple individual or jointly-trained networks, with lower number of trainable parameters.
