Image inpainting is an important part of image science,but in the past,researches were focused on gray value image inpainting.In this paper,we investigate the inpainting effects of some variational models of color ima...Image inpainting is an important part of image science,but in the past,researches were focused on gray value image inpainting.In this paper,we investigate the inpainting effects of some variational models of color image diffusion.Five variational models for color image inpainting are proposed and their Split Bregman algorithms are designed.Their regularizers are LTV(Layered Total Variation) regularizer,CTV(Color Total Variation) regularizer,MTV(Multichannel Total Variation) regularizer,PA(Polyakov Action) regularizer and RPA(Reduced Polyakov Action) regularizer respectively.In order to compare their performances,we use the same data term...Some numerical experiments show the differences of the above mentioned models for color image inpainting.展开更多
To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the freque...To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.展开更多
文摘Image inpainting is an important part of image science,but in the past,researches were focused on gray value image inpainting.In this paper,we investigate the inpainting effects of some variational models of color image diffusion.Five variational models for color image inpainting are proposed and their Split Bregman algorithms are designed.Their regularizers are LTV(Layered Total Variation) regularizer,CTV(Color Total Variation) regularizer,MTV(Multichannel Total Variation) regularizer,PA(Polyakov Action) regularizer and RPA(Reduced Polyakov Action) regularizer respectively.In order to compare their performances,we use the same data term...Some numerical experiments show the differences of the above mentioned models for color image inpainting.
基金supported by the National Natural Science Foundation of China(No.NSFC 41204101)Open Projects Fund of the State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(No.PLN201733)+1 种基金Youth Innovation Promotion Association of the Chinese Academy of Sciences(No.2015051)Open Projects Fund of the Natural Gas and Geology Key Laboratory of Sichuan Province(No.2015trqdz03)
文摘To improve the anti-noise performance of the time-domain Bregman iterative algorithm,an adaptive frequency-domain Bregman sparse-spike deconvolution algorithm is proposed.By solving the Bregman algorithm in the frequency domain,the influence of Gaussian as well as outlier noise on the convergence of the algorithm is effectively avoided.In other words,the proposed algorithm avoids data noise effects by implementing the calculations in the frequency domain.Moreover,the computational efficiency is greatly improved compared with the conventional method.Generalized cross validation is introduced in the solving process to optimize the regularization parameter and thus the algorithm is equipped with strong self-adaptation.Different theoretical models are built and solved using the algorithms in both time and frequency domains.Finally,the proposed and the conventional methods are both used to process actual seismic data.The comparison of the results confirms the superiority of the proposed algorithm due to its noise resistance and self-adaptation capability.
