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  • 标题:An Improved Super-resolution Image Reconstruction Algorithm
  • 本地全文:下载
  • 作者:Yong Yin ; Qianqian Ruan ; Tao Zhang
  • 期刊名称:International Journal of Signal Processing, Image Processing and Pattern Recognition
  • 印刷版ISSN:2005-4254
  • 出版年度:2016
  • 卷号:9
  • 期号:3
  • 页码:103-112
  • DOI:10.14257/ijsip.2016.9.3.09
  • 出版社:SERSC
  • 摘要:The paper introduces the Keren registration method and points out its disadvantage which means it will become inaccuracy on the large scale parameters. To reduce the error on large scale parameters of Keren registration, a two step method is proposed, which the phase correlation algorithm is used to estimate the large translation and rotation angle roughly and the improved Keren algorithm is used to estimate accurately the small translation and rotation angle. The experimental results show that the two step method makes less absolute error of angle than Keren method in the situation of large translation and rotation angle. A new method of estimating the standard deviation of noise is introduced to the robust certainty function, which reduces the impact of noise in the process of interpolation using normalized convolution algorithm. By the edge detection of fusion image in the first stage of the interpolation process of normalized convolution algorithm, a calculation method of long axis and short axis of the structure self-adaptive function is improved. The experimental results show that the proposed interpolation method can improve the performance of the original algorithm and enhance the effect of image super-resolution reconstruction.
  • 关键词:super-resolution image reconstruction; image registration; phase ; correlation; Keren algorithm; normalized convolution
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