用生成对抗网络实现灰度图自动着色，提升颜色多样性与真实感。
Automatic Image Colorization with Convolutional Neural Networks and Generative Adversarial Networks
- 将着色问题转为分类任务，结合对抗学习增强颜色多样性。
- 利用大量彩色图像数据学习场景语义与纹理的颜色先验。
- 适合图像修复、动画上色等需要自然色彩还原的应用场景。
图像着色是为灰度图像添加颜色的任务，近年来在计算机视觉中备受关注，广泛应用于颜色复原和自动动画着色等领域。该问题具有高度病态性，因丢失了图像的三个维度中的两个，导致自由度极高。然而，场景语义和表面纹理可提供重要颜色线索：天空通常为蓝色，云朵多为白色，草地常为绿色。由于任意彩色图像均可作为训练样本，存在海量可用于学习这些先验知识的数据。早期方法将其视为回归任务，忽略了颜色预测的多模态特性。本文探索基于分类与对抗学习的自动图像着色方法，基于已有工作构建模型，在特定场景下进行改进并开展对比实验。
原文摘要 · Abstract (English)
Image colorization, the task of adding colors to grayscale images, has been the focus of significant research efforts in computer vision in recent years for its various application areas such as color restoration and automatic animation colorization [15, 1]. The colorization problem is challenging as it is highly ill-posed with two out of three image dimensions lost, resulting in large degrees of freedom. However, semantics of the scene as well as the surface texture could provide important cues for colors: the sky is typically blue, the clouds are typically white and the grass is typically green, and there are huge amounts of training data available for learning such priors since any colored image could serve as a training data point [20]. Colorization is initially formulated as a regression task[5], which ignores the multi-modal nature of color prediction. In this project, we explore automatic image colorization via classification and adversarial learning. We will build our models on prior works, apply modifications for our specific scenario and make comparisons.
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