Multiplicative noise removal based on total generalized variation
MetadataShow full item record
© Springer Nature Singapore Pte Ltd. 2018. When the first order variational models are used for multiplicative noise removal, there always some staircase effect, contract reduction, and corner smearing. In this paper, we will design a new second order variational model based on the total generalized variation (TGV) regularizer to solve these problems. The second order variation model is proposed originally for additive noise removal and we revise it in this paper for multiplicative noise removal. For the sake of computational efficiency, we transform this proposed model into a Split Bregman iterative scheme by introducing some auxiliary variables and iterative parameters, and then solve it via alternating optimization strategy. In order to speed up the computational efficiency, we also apply the fast Fourier transform (FFT), generalized soft threshold formulas and gradient descent method to the related sub-problems in each step. The experimental results show that in comparison with the first order total variation (TV) model, the proposed TGV model can effectively overcome the staircase effect; Also in comparison with the second order bounded Hessian regularization, the TGV model shows the advantage of preserving corners and edges in images.
Showing items related by title, author, creator and subject.
Liang, A.; Pathirage, C.; Wang, C.; Liu, Wan-Quan; Li, L.; Duan, J. (2016)In this paper we address the challenge of performing face recognition on human faces that are wearing glasses. This is a common problem for face recognition and automatic identity checking at airports, as passengers ...
Wang, Y.; Peng, J.; Zhao, Q.; Leung, Yee-Hong; Zhao, X.; Meng, D. (2018)Hyperspectral images (HSIs) are often corrupted by a mixture of several types of noise during the acquisition process, e.g., Gaussian noise, impulse noise, dead lines, stripes, etc. Such complex noise could degrade the ...
Lentati, L.; Alexander, P.; Hobson, P.; Feroz, F.; van Haasteren, R.; Lee, K.; Shannon, Ryan (2014)A new Bayesian software package for the analysis of pulsar timing data is presented in the form of TEMPONEST which allows for the robust determination of the non-linear pulsar timing solution simultaneously with a range ...