According to the traditional ROF model is prone to produce the staircase effect when repairring images
a new adaptive fractional TV repair algorithm is proposed. The algorithm uses the globality of fractional differential to extend the regular term of ROF repair model from first order differential to fractional differential
which effectively solves the staircase effect of traditional ROF model in repairing image. In addition
in order to maintain the edge of the repair image better
an adaptive edge instruction operator is designed on the fractional differential regular term. Because of the similarity between the model and the optimization model of the saddle point structure
the new model is solved by using the primitive dual algorithm based on the prerequisite
and the adaptive convolution filter function is used to control the diffusion of the new model repair. The experimental results show that compared with the traditional TV and TGV repair algorithm
the adaptive fractional TV repair algorithm proposed in this paper has better repair effect and can be better repaired for image damage such as patches
scratches and text.
关键词
Keywords
references
Wen Y W, Chan R H, Yip A M. A primal-dual method for total-variation-based wavelet domain inpainting[J]. IEEE Transactions on Image Processing, 2012, 21(1): 106-114. DOI: 106
Xu K, Wang N, Gao X. Image inpainting based on sparse representation with dictionary pre-clustering[C]//Chinese Conference on Pattern Recognition. Singapore, Springer, 2016: 245-258.
Zhang J, Wei Z. Fractional variational model and algorithm for image denoising[C]//Natural Computation, 2008. ICNC'08. Fourth International Conference on, China. Jinan, IEEE, 2008: 524-528.
Zhou T, Johnson B, Li R. Patch-based texture synthesis for image inpainting[J]. 电子与信息学报, 2009, 31 (6): 1319-1322. DOI: 1319
Chan T F, Shen J. Variational image inpainting[R]. USA, California Univ Los Angeles Dept Of Mathematics, 2005.
Rudin L I, Osher S, Fatemi E. Nonlinear total variation based noise removal algorithms[J]. Physica D: Nonlinear Phenomena, 1992, 60(1/4): 259-268. DOI: 259
Tian D, Xue D, Wang D. A fractional-order adaptive regularization primal-dual algorithm for image denoising[J]. Information Sciences, 2015, 296(7): 147-159. DOI: 147
Chambolle A, Pock T. A first-order primal-dual algorithm for convex problems with applications to imaging[J]. Journal of Mathematical Imaging and Vision, 2011, 40(1): 120-145. DOI: 120