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- Title
基于邻域回归的医学图像超分辨率重建.
- Authors
端木春江; 沈碧婷
- Abstract
This paper proposed a super-resolution method based on neighborhood regression of internal examples, in order to improve the resolution of medical images. First, it treated the input low-resolution image as a high-resolution image to construct an internal image training set based on its own instance, no longer dependent on the external training set. Then, it divided the high-resolution reconstruction into high-frequency reconstruction and low-frequency reconstruction, neighborhood regression method reconstructed the high-frequency detail part of the image, and bicubic interpolation method reconstructed the low-frequency part. Finally, iterative combination method combined the high-frequency component and the low-frequency component to get a high-resolution image of the final output. The experimental results show that the proposed method outperforms the traditional super-resolution reconstruction algorithm, and the reconstructed medical image visual effect is more realistic.
- Subjects
IMAGE reconstruction algorithms; DEPENDENTS; ALGORITHMS; NEIGHBORHOODS; INTERPOLATION; IMAGE
- Publication
Application Research of Computers / Jisuanji Yingyong Yanjiu, 2020, Vol 37, Issue 12, p3792
- ISSN
1001-3695
- Publication type
Article
- DOI
10.19734/j.issn.1001-3695.2019.06.0311