• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

计算机工程与科学 ›› 2025, Vol. 47 ›› Issue (3): 513-523.

• 图形与图像 • 上一篇    下一篇

结合图像分解和自稀疏模糊聚类的情感颜色迁移

谢斌,李燕伟,杨舒敏,徐燕,王冠超   

  1. (江西理工大学信息工程学院,江西 赣州 341000)

  • 收稿日期:2023-11-21 修回日期:2024-01-24 出版日期:2025-03-25 发布日期:2025-04-02
  • 基金资助:
    国家自然科学基金(61972264);江西省自然科学基金(20192BAB207036);江西理工大学博士启动基金(20520010058)

Emotional color transfer combining image decomposition and self-sparse fuzzy clustering

XIE Bin,LI Yanwei,YANG Shumin,XU Yan,WANG Guanchao   

  1. (School of Information Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China)
  • Received:2023-11-21 Revised:2024-01-24 Online:2025-03-25 Published:2025-04-02

摘要: 针对传统情感颜色迁移方法存在层次感欠缺、细节模糊和视觉效果不佳等问题,结合图像分解和自稀疏模糊聚类提出了一种新的迁移方法。首先,为了更好地维持图像的细节,引入基于低秩纹理先验的卡通纹理分解将源图像分为包含主要颜色的平滑图和包含局部信息的纹理图。其次,利用自稀疏模糊聚类方法得到平滑图的主要代表性颜色和其对应的分割区域,让图像在提取过程中更好地保留源图像的层次结构。最后,设计了一种自适应亮度修正的防溢出策略,并在此基础上提出了一种新的情感颜色迁移方法,旨在使结果图像更加符合人眼的视觉识别特性。实验结果表明,所提出的方法得到了质量更高的迁移结果图像,且在主客观评价方面都表现更优。

关键词: 情感颜色迁移, 自稀疏模糊聚类, 图像分解, 自适应亮度修正, 平滑图

Abstract: Aming at the problems of lack of layering, blurring of details and poor visual effect in traditional emotion color transfer methods, a new transfer method is proposed by combining image decomposition and self-sparse fuzzy clustering. Firstly, in order to better maintain the details of the image, a cartoon texture decomposition based on low-rank texture prior is introduced to divide the source image into a smoothed map containing the main colors and a texture map with local information. Secondly, the self-sparse fuzzy clustering method is used to obtain the main representative colors and corresponding segmentation regions of the smooth map, enabling the result image to better retain the structure  of the source image.  Finally, an adaptive brightness correction anti-overflow strategy is designed, and based on this, a new emotional color transfer method is proposed to make the result image more consistent with human visual characteristics. Experimental results show that the proposed  method produces higher- quality transfer result images and performs better in both subjective and objective evaluations.

Key words: emotional color transfer, self-sparse fuzzy clustering, image decomposition, adaptive brightness correction, smoothing map