Abstract
The traditional projection onto convex set (POCS) algorithm can reconstruct a low resolution (LR) image, but it is contradictory in retaining image detail and denoising, so the quality of a reconstructed image is limited. To avoid defects of POCS and obtain higher resolution, the image denoising idea based on sparse representation is led into this paper. Sparse representation can learn well the optimized overcomplete sparse dictionary of an image, which has self-adaptive property to image data and can describe image essential features so as to implement the goal of denoising efficiently. At present, K- singular value decomposition (K-SVD) is the emerging image processing method of sparse representation and has been used widely in image denoising. Therefore, combined the advantages of K-SVD and POCS, a new image ISR method is explored here. In terms of signal noise ratio (SNR) values and the visual effect of reconstructed images, simulation results show that our method proposed has clear improvement in image resolution and can retain image detail well.
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Acknowledgements
This work was supported by the grants from National Nature Science Foundation of China (Grant No. 61373098 and 61370109), the grant from Natural Science Foundation of Anhui Province (No. 1308085MF85).
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Shang, L., Liu, Sf., Sun, Zl. (2015). Image Super-Resolution Reconstruction Based on Sparse Representation and POCS Method. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_34
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DOI: https://doi.org/10.1007/978-3-319-22180-9_34
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