Research on Image Super-Resolution Reconstruction of Optical Image
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.
KeywordsSparse representation Super-resolution reconstruction Dictionary construction Gradient estimation
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).
- 4.Zhan S, Fang Q, Yang F, et al. Image super-resolution reconstruction via improved dictionary learning based on coupled feature space. Acta Electron Sin. 2016;44(5):1189–95.Google Scholar
- 5.Li J, Wu J, Chen Z, et al. Self-learning image super-resolution method based on sparse representation. Chin J Sci Instrum. 2015;36(1):194–200.Google Scholar