文章引用说明 更多>> (返回到该文章)

张德丰. 数字图像处理(MATLAB版) [M]. 北京: 人民邮电出版社, 2009: 15-30.

被以下文章引用:

  • 标题: 基于结构稀疏的图像修复实验设计The Design of Experiment on Image Inpainting Using Structure Sparisity

    作者: 胡晨, 于文静, 张晓宇, 方成, 杨尊涵, 陈宁

    关键字: 图像修复, 结构稀疏, 稀疏表达Image Inpainting, Structure Sparisity, Sparisity Expression

    期刊名称: 《Advances in Education》, Vol.6 No.3, 2016-05-19

    摘要: 对于自然图像稀疏先验的大量研究工作,为利用图像的结构稀疏性实现图像的修复奠定了基础。为了使学生了解结构稀疏的概念,并将这一特征应用于图像修复,本文设计了基于结构稀疏的图像修复实验。通过本实验,旨在使学生了解结构稀疏性及图像块稀疏表达的概念及应用,通过图像块稀疏实现图像修复,比较结构稀疏实现图像修复与经典图像修复的不同。文中提供不同算法在修复过程中的实现方式的不同以及每种方法的优缺点,帮助学生更好理解算法的实现过程和图像修复的意义。 The investigation on the sparisity of natural image is the basis of image inpainting using structure sparisity of images. The experiment is designed to deepen students’ understanding about the concept of structure sparisity and the implementation to the image inpainting technology. In the experiment, the conception and implementation of structure sparisity and sparisity expression are introduced to achieve image inpainting by image patch sparisity. The experiments and comparisons with the classical algorithm are performed. In the paper, the process, virtue and shortage of inpainting in different inpainting algorithms are compared, which is helpful for students to understand the process and the meaning of image inpainting.

在线客服:
对外合作:
联系方式:400-6379-560
投诉建议:feedback@hanspub.org
客服号

人工客服,优惠资讯,稿件咨询
公众号

科技前沿与学术知识分享