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Research on Image Super-Resolution Reconstruction of Optical Image

  • Aiping JiangEmail author
  • Xinwei Li
  • Han Gao
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 516)

Abstract

Currently, the image super-resolution reconstruction method based on sparse representation has limited ability to process the details of the edge. Therefore, based on the dictionary learning, the local variance feature edge gradient estimation image fast super-resolution reconstruction is improved and optimized based on dictionary training. The dictionary training process includes cluster analysis of high-resolution images, local variance extraction, and sparse filtering. The reconstruction process includes local variance detection of the low-resolution image and threshold judgment, and then the image is reconstructed according to the gradient value.

Keywords

Sparse representation Super-resolution reconstruction Dictionary construction Gradient estimation 

Notes

Acknowledgements

This work is supported in part by the National Natural Science Foundation China (61601174), in part by the Postdoctoral Research Foundation of Heilongjiang Province (LBH-Q17150), in part by the Science and Technology Innovative Research Team in Higher Educational Institutions of Heilongjiang Province (No. 2012TD007),in part by the Fundamental Research Funds for the Heilongjiang Provincial Universities (KJCXZD201703), and in part by the Science Foundation of Heilongjiang Province of China (F2018026).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Heilongjiang UniversityHarbinChina

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