论文标题
M2-NET：多阶段的多个阶段镜面突出显示和拆除多场景
M2-Net: Multi-stages Specular Highlight Detection and Removal in Multi-scenes
论文作者
论文摘要
在本文中，我们提出了一个新颖的统一框架，以突出多片中的检测和去除，包括合成图像，面部图像，自然图像和文本图像。该框架由三个主要组件组成，突出显示了特征提取器模块，突出显示粗卸下模块和凸显的精炼拆卸模块。首先，高光功能提取器模块可以将突出显示功能和非高光功能与原始突出显示图像直接分开。然后，使用粗大的重点拆卸网络获得了突出显示的拆卸图像。为了进一步提高突出显示效果，最终，使用基于上下文的重点注意机制，使用精炼的突出显示模块获得了精制的突出显示图像。多个场景中的广泛实验结果表明，所提出的框架可以获得突出显示删除的出色视觉效果，并实现最新的结果，从而获得了几种定量评估指标。我们的算法首次在视频重点删除中首次应用，并有令人鼓舞的结果。
In this paper, we propose a novel uniformity framework for highlight detection and removal in multi-scenes, including synthetic images, face images, natural images, and text images. The framework consists of three main components, highlight feature extractor module, highlight coarse removal module, and highlight refine removal module. Firstly, the highlight feature extractor module can directly separate the highlight feature and non-highlight feature from the original highlight image. Then highlight removal image is obtained using a coarse highlight removal network. To further improve the highlight removal effect, the refined highlight removal image is finally obtained using refine highlight removal module based on contextual highlight attention mechanisms. Extensive experimental results in multiple scenes indicate that the proposed framework can obtain excellent visual effects of highlight removal and achieve state-of-the-art results in several quantitative evaluation metrics. Our algorithm is applied for the first time in video highlight removal with promising results.
