• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>International symposium on multispectral image processing and pattern recognition >Sparse Representation via Learned Dictionaries for X-ray Angiogram Image Denoising
【24h】

Sparse Representation via Learned Dictionaries for X-ray Angiogram Image Denoising

机译：通过学习词典对X射线血管造影图像去噪的稀疏表示

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

X-ray angiogram image denoising is always an active research topic in the field of computer vision. In particular, the denoising performance of many existing methods had been greatly improved by the widely use of nonlocal similar patches. However, the only nonlocal self-similar (NSS) patch-based methods can be still be improved and extended. In this paper, we propose an image denoising model based on the sparsity of the NSS patches to obtain high denoising performance and high-quality image. In order to represent the sparsely NSS patches in every location of the image well and solve the image denoising model more efficiently, we obtain dictionaries as a global image prior by the K-SVD algorithm over the processing image; Then the single and effectively alternating directions method of multipliers (ADMM) method is used to solve the image denoising model. The results of widely synthetic experiments demonstrate that, owing to learned dictionaries by K-SVD algorithm, a sparsely augmented lagrangian image denoising (SALID) model, which perform effectively, obtains a state-of-the-art denoising performance and better high-quality images. Moreover, we also give some denoising results of clinical X-ray angiogram images.
机译：X射线血管造影图像降噪一直是计算机视觉领域的活跃研究主题。特别地，通过广泛使用非局部相似补丁，大大改善了许多现有方法的去噪性能。但是，仍然可以改进和扩展仅基于非本地自相似（NSS）补丁的方法。在本文中，我们提出了一种基于NSS补丁稀疏性的图像去噪模型，以获得高去噪性能和高质量图像。为了很好地表示图像中每个位置的稀疏NSS斑块并更有效地求解图像去噪模型，我们先通过K-SVD算法在处理图像上获得字典作为全局图像。然后，采用有效的单方向交替乘数法（ADMM）求解图像去噪模型。广泛的综合实验结果表明，由于使用K-SVD算法学习了字典，因此有效执行的稀疏拉格朗日图像降噪（SALID）模型获得了最新的降噪性能和更好的高质量图片。此外，我们还给出了一些临床X射线血管造影图像的去噪结果。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号