@Article{csse.2023.028906,
AUTHOR = {Israa Ismail, Mohamed Meselhy Eltoukhy, Ghada Eltaweel},
TITLE = {Super-Resolution Based on Curvelet Transform and Sparse Representation},
JOURNAL = {Computer Systems Science and Engineering},
VOLUME = {45},
YEAR = {2023},
NUMBER = {1},
PAGES = {167--181},
URL = {http://www.techscience.com/csse/v45n1/49310},
ISSN = {},
ABSTRACT = {Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s). In this paper, we proposed a single image super-resolution algorithm. It uses the nonlocal mean filter as a prior step to produce a denoised image. The proposed algorithm is based on curvelet transform. It converts the denoised image into low and high frequencies (sub-bands). Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands. In parallel, we applied sparse representation with over complete dictionary for the denoised image. The proposed algorithm then combines the dictionary learning in the sparse representation and the interpolated sub-bands using inverse curvelet transform to have an image with a higher resolution. The experimental results of the proposed super-resolution algorithm show superior performance and obviously better-recovering images with enhanced edges. The comparison study shows that the proposed super-resolution algorithm outperforms the state-of-the-art. The mean absolute error is 0.021 ± 0.008 and the structural similarity index measure is 0.89 ± 0.08.},
DOI = {10.32604/csse.2023.028906}
}