.png){kind=icon}
返回
Variational Exemplar-Based Image Colorization
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2013.2288929.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we address the problem of recovering a color image from a grayscale one. The input color data comes from a source image considered as a reference image. Reconstructing the missing color of a grayscale pixel is here viewed as the problem of automatically selecting the best color among a set of color candidates while simultaneously ensuring the local spatial coherency of the reconstructed color information. To solve this problem, we propose a variational approach where a specific energy is designed to model the color selection and the spatial constraint problems simultaneously. The contributions of this paper are twofold. First, we introduce a variational formulation modeling the color selection problem under spatial constraints and propose a minimization scheme, which computes a local minima of the defined nonconvex energy. Second, we combine different patch-based features and distances in order to construct a consistent set of possible color candidates. This set is used as input data and our energy minimization automatically selects the best color to transfer for each pixel of the grayscale image. Finally, the experiments illustrate the potentiality of our simple methodology and show that our results are very competitive with respect to the state-of-the-art methods.
Keyword:
Image enhancement
image restoration
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
.png){kind=icon}
Rapid charging strategy in the constant voltage mode for a high power Li-Ion battery一种大功率锂离子电池的恒压快速充电策略
PREAI
分享收藏分享收藏
查看更多内容