计算机科学 ›› 2010, Vol. 37 ›› Issue (10): 246-247.

• 图形图像 • 上一篇    下一篇

图像1 DFFT-MP稀疏分解算法研究

李小燕,尹忠科   

  1. (西南交通大学信息科学与技术学院 成都610031)
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金(60772084)资助。

Image Spare Decomposition Algorithm Based on MP and 1DFFT

LI Xiao-yan,YIN Zhong-ke   

  • Online:2018-12-01 Published:2018-12-01

摘要: 针对图像稀疏分解速度慢和重建图像视觉效果不好的问题,提出了一种基于MP和一维FFT、的图像稀疏分解算法。算法中把二维图像按行抽取成一维信号,同样地,把过完备原子库中的原子按行抽取成一维原子,然后把二维图像或图像残差与原子的内积运算转化为一维信号或信号残差与一维原子的互相关运算,最后利用一维FFT方法计算一维信号与原子的互相关运算。通过实验验证表明,在重建图像的质量没有改变的前提下,当图像大小为512 X512时,一维FF7图像稀疏分解算法的速度比二维FFT提高了2. 11倍。

关键词: 图像处理,稀疏分解,MP,过完备原子库,FFT

Abstract: There arc two main problems in image sparse decomposition that the speed is timcconsuming and the visual effect of reconstructed image is not very good. This paper introduced a new algorithm based on MP and IDFI门in image sparse decomposition. Two-dimensional image to be decomposed is converted into ono-dimensional signal. Likewise, the atoms of the over-complete dictionary arc converted into oncdimensional atoms. Then, the inner product operation between image or residual image and atom is transformed into the cross-correlation operation of one-dimensional signals.Finally, the method of FF"T realizes the cross correlation operation between oncdimensional signal and oncdimensional atom. Experimental results show that, when the size of the image is 512 X 512,compared with the two-imensional FIST method, the proposed algorithm speeds up a little more than 2. 11 times without any loss of the reconstructed image quality.

Key words: Image processing, Sparse decomposition, MP, Over-complete dictionary, FIST

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