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.
翻译:在本文中,我们提出一个新的统一框架,以突出多层图像(包括合成图像、脸部图像、自然图像和文本图像)的探测和清除;框架由三个主要部分组成,突出地物提取模块,突出粗体去除模块,突出精细去除模块。首先,突出地物提取模块可以直接将突出物特征和非高光特征与原始突出物图像区分开来。然后,利用粗体色突出物去除网络来突出清除图像。为了进一步改善突出物去除效果,利用基于背景突出物关注机制的精细突出清除模块最终获得了精细的突出物去除图像。在多个场面的广泛实验结果表明,拟议的框架可以取得突出去除物的极佳视觉效果,并在若干定量评估指标中取得最先进的结果。我们的算法首次在视频中以有希望的结果突出去除物中应用。