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Proceedings Paper

Alternating proximal algorithm for L1/TVp(0 < p < 1) image recovery
Author(s): Xiao Jin; Yuan Xiao
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Paper Abstract

Alternating proximal algorithm is presented for L1/TVp (0 < p < 1) nonconvex variational model, which typically outperforms popular models with convex variational models in restoring sparse images with corruption by the impulse noise or other outliers. Numerical experiments are reported to illustrate the effectiveness of this algorithm.

Paper Details

Date Published: 6 May 2019
PDF: 9 pages
Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692K (6 May 2019); doi: 10.1117/12.2524391
Show Author Affiliations
Xiao Jin, Lingnan Normal Univ. (China)
Yuan Xiao, Lingnan Normal Univ. (China)

Published in SPIE Proceedings Vol. 11069:
Tenth International Conference on Graphics and Image Processing (ICGIP 2018)
Chunming Li; Hui Yu; Zhigeng Pan; Yifei Pu, Editor(s)

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