Semi-blind image deblurring based on framelet prior
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Link
https://link.springer.com/content/pdf/10.1007/s11760-023-02926-z.pdf
Reference18 articles.
1. Parvaz, R.: Image restoration with impulse noise based on fractional-order total variation and framelet transform. Signal Image Video Process., pp. 1-9 (2023)
2. Yin, M., Adam, T., Paramesran, R., Hassan, M.F.: An $$l0$$-overlapping group sparse total variation for impulse noise image restoration. Signal Process. Image Commun. 102, 116620 (2022)
3. Hansen, P.C., Nagy, J.G., O’leary, D.P.: Deblurring images: matrices, spectra, and filtering. In: Society for Industrial and Applied Mathematics, (2006)
4. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear total variation based noise removal algorithms. Physica D 60(1–4), 259–268 (1992)
5. Adam, T., Paramesran, R.: Hybrid non-convex second-order total variation with applications to non-blind image deblurring. SIViP 14(1), 115–123 (2020)
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