论文标题
Palgan：带有调色板生成对抗网络的图像着色
PalGAN: Image Colorization with Palette Generative Adversarial Networks
论文作者
论文摘要
多模式的歧义和色彩出血在着色方面仍然具有挑战性。为了解决这些问题，我们提出了一种新的基于GAN的配色方法Palgan，并与调色板的估计和色彩关注集成在一起。为了避免多模式问题，我们提出了一种新的着色公式，该配色首先从输入灰色图像中估算概率调色板，然后通过生成模型在调色板上进行色彩分配。此外，我们以色彩的注意处理颜色出血。它通过考虑语义和强度相关性来研究颜色亲和力。在广泛的实验中，帕尔根（Palgan）在定量评估和视觉比较方面的表现优于最先进的实验，提供了显着的多样化，对比和边缘保护外观。通过调色板设计，我们的方法即使与无关的上下文也可以在图像之间进行色彩传递。
Multimodal ambiguity and color bleeding remain challenging in colorization. To tackle these problems, we propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. To circumvent the multimodality issue, we present a new colorization formulation that estimates a probabilistic palette from the input gray image first, then conducts color assignment conditioned on the palette through a generative model. Further, we handle color bleeding with chromatic attention. It studies color affinities by considering both semantic and intensity correlation. In extensive experiments, PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances. With the palette design, our method enables color transfer between images even with irrelevant contexts.
