Abstract
The necessity to improve image resolution is of great concern in multiple diverse fields such as: medicine, communications, or satellite and underwater applications. A high variety of techniques for image enhancement has been proposed in the literature, being a trade-off the relation between the computation time and the quality of the obtained results. This work is focused on a test environment that permits to objectively compare the quality enhancement of images processed by two different improvement methods: bilinear interpolation and Super-Resolution (SR), presenting how these results relate to the computation time. The objective comparison is based on the PSNR (Peak Signal-to-Noise Ratio) and the SSIM (Structural SIMilarity). © 2013 Springer-Verlag Berlin Heidelberg.
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Quevedo, E., Horat, D., Callicó, G. M., & Tobajas, F. (2013). Computation time optimization in super-resolution applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8112 LNCS, pp. 101–108). Springer Verlag. https://doi.org/10.1007/978-3-642-53862-9_14
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