论文标题
使用U-NET与跳过连接和景观图像上的融合图像进行图像着色
Image Colorization using U-Net with Skip Connections and Fusion Layer on Landscape Images
论文作者
论文摘要
我们提出了一种新颖的技术，可以自动化着结合U-NET模型和融合层特征的灰度图像。这种方法允许模型从预训练的U-NET中学习图像的着色。此外，融合层用于合并本地信息结果，取决于小图像贴片与每个类上整个图像的全局先验，从而形成更引人注目的着色结果。最后，我们通过用户研究评估来验证我们的方法，并将其与最先进的方法进行比较，从而改善。
We present a novel technique to automatically colorize grayscale images that combine the U-Net model and Fusion Layer features. This approach allows the model to learn the colorization of images from pre-trained U-Net. Moreover, the Fusion layer is applied to merge local information results dependent on small image patches with global priors of an entire image on each class, forming visually more compelling colorization results. Finally, we validate our approach with a user study evaluation and compare it against state-of-the-art, resulting in improvements.
