Abstract
A new image super-resolution reconstruction (SRR) method, combined a modified K-means based Singular Value Decomposition (K-SVD) and Regularized Adaptive Matching Pursuit (RAMP) algorithm is proposed in this paper. In the modified K-SVD algorithm, the maximum sparsity is considered. First, the K-SVD denoising model is first to preprocess the Low Resolution (LR) images. And then, the high-resolution (HR) and LR images are both trained by the RAMP based K-SVD algorithm. The LR and HR dictionaries are also classed by the K-mean method. In test, a human-made and real LR image, namely millimeter wave (MMW) image are respectively used to testify our method proposed. Further, compared our image SRR method with methods of the basic K-SVD and RAMP, experimental results testified the validity of our method proposed.
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Shang, L., Liu, T., Sun, Zl. (2014). Image Super-resolution Reconstruction Utilizing the Combined Method of K-SVD and RAMP. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_51
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DOI: https://doi.org/10.1007/978-3-319-09333-8_51
Publisher Name: Springer, Cham
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