论文标题
水疗形式：变压器图像阴影检测和通过空间注意
SpA-Former: Transformer image shadow detection and removal via spatial attention
论文作者
论文摘要
在本文中，我们提出了一种端到端的水疗形式，以从单个阴影图像中恢复无阴影的图像。与需要两个步骤进行阴影检测然后删除阴影的传统方法不同，Spa-Former将这些步骤统一为一个，这是一个单阶段网络，能够直接学习阴影和无阴影之间的映射功能，因此不需要单独的阴影检测。因此，SPA形式适应于实际图像去阴影，以适应投影在不同语义区域上的阴影。水疗形式由变压器层和一系列关节傅立叶变压残留块和两轮关节空间注意力组成。本文中的网络能够在达到非常快速的处理效率的同时处理任务。 我们的代码将在https://github.com/zhangbaijin/spa-former-hadow-removal上重新发布
In this paper, we propose an end-to-end SpA-Former to recover a shadow-free image from a single shaded image. Unlike traditional methods that require two steps for shadow detection and then shadow removal, the SpA-Former unifies these steps into one, which is a one-stage network capable of directly learning the mapping function between shadows and no shadows, it does not require a separate shadow detection. Thus, SpA-former is adaptable to real image de-shadowing for shadows projected on different semantic regions. SpA-Former consists of transformer layer and a series of joint Fourier transform residual blocks and two-wheel joint spatial attention. The network in this paper is able to handle the task while achieving a very fast processing efficiency. Our code is relased on https://github.com/zhangbaijin/SpA-Former-shadow-removal
