Abstract
Image super-resolution reconstruction is a high-resolution image that is reconstructed from a low-resolution image. The learning-based algorithm is one of the more effective algorithms for image super-resolution reconstruction, and the core idea of the algorithm is to use the sample library to train the information of the image in order to increase the high-frequency information of the test image and achieve the purpose of image super-resolution reconstruction. In this paper, we propose a new image super-resolution algorithm based on morphological component analysis and dictionary learning. Firstly we make independent component analysis for image denoising processing by the K-SVD method. And then, MCA algorithm is utilized to efficiently decompose low-resolution images into texture part and structure part. And the K-SVD method is used to make dictionary training of low-resolution images. The method not only improves the robustness of the images, but also adopts different reconstruction algorithms for the different characteristics of the texture and structure parts, which better retains the details of the images and improves the quality of the reconstructed images.
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Acknowledgements
This work was supported by the talent project of "Qingtan Scholar" of Zaozhuang University, Youth Innovation Team of Scientific Research Foundation of the Higher Education Institutions of Shandong Province, China (No. 2019KJM006), the Key Research Program of the Science Foundation of Shandong Province (ZR2020KE001), the PhD research startup foundation of Zaozhuang University (No.2014BS13), and Zaozhuang University Foundation (No. 2015YY02).
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Yang, W., Yang, B., Li, J., Sun, Z. (2022). Image Super-Resolution Reconstruction Based on MCA and ICA Denoising. In: Huang, DS., Jo, KH., Jing, J., Premaratne, P., Bevilacqua, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2022. Lecture Notes in Computer Science, vol 13393. Springer, Cham. https://doi.org/10.1007/978-3-031-13870-6_48
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DOI: https://doi.org/10.1007/978-3-031-13870-6_48
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