Abstract
For decades, image super-resolution reconstruction is one of the research hotspots in the field of image processing. This paper presents a novel approach to deal with single image super-resolution. It’s proven that image patches can be represented as a sparse linear combination of elements from a well-chosen over-complete dictionary. Using a dictionary of image patches learned by K-SVD algorithm, we exploit the similarity of sparse representations to form an image with edge-preserving information as guidance. After optimizing the guide image, the joint bilateral filter is applied to transfer the edge and contour information to gain smooth edge details. Merged with texture-preserving images, experiments show that the reconstructed images have higher visual quality compared to other similar SR methods.
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Sun, G., Shen, Z. (2010). Single Image Super-Resolution via Edge Reconstruction and Image Fusion. In: Kim, Th., Pal, S.K., Grosky, W.I., Pissinou, N., Shih, T.K., Ślęzak, D. (eds) Signal Processing and Multimedia. MulGraB SIP 2010 2010. Communications in Computer and Information Science, vol 123. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17641-8_3
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DOI: https://doi.org/10.1007/978-3-642-17641-8_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17640-1
Online ISBN: 978-3-642-17641-8
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