Alternating proximal algorithm for  L1/TVp(0 < p < 1) image recovery

Xiao Jin; Yuan Xiao

doi:10.1117/12.2524391

6 May 2019 Alternating proximal algorithm for L₁/TV^p(0 < p < 1) image recovery

Xiao Jin, Yuan Xiao

Proceedings Volume 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018); 110692K (2019) https://doi.org/10.1117/12.2524391
Event: Tenth International Conference on Graphic and Image Processing (ICGIP 2018), 2018, Chengdu, China

Abstract

Alternating proximal algorithm is presented for L₁/TV^p (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.

Citation Download Citation

Xiao Jin and Yuan Xiao "Alternating proximal algorithm for L₁/TV^p(0 < p < 1) image recovery", Proc. SPIE 11069, Tenth International Conference on Graphics and Image Processing (ICGIP 2018), 110692K (6 May 2019); https://doi.org/10.1117/12.2524391

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