Volume XLII-2/W12
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 219-224, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-219-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2/W12, 219-224, 2019
https://doi.org/10.5194/isprs-archives-XLII-2-W12-219-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  09 May 2019

09 May 2019

AN ADAPTIVE VARIATIONAL MODEL FOR MEDICAL IMAGES RESTORATION

T. T. T. Tran1, C. T. Pham2, A. V. Kopylov3, and V. N. Nguyen2 T. T. T. Tran et al.
  • 1The University of Danang-University of Economics, 71 Ngu Hanh Son, Danang, Viet Nam
  • 2The University of Danang-University of Science and Technology, 54 Nguyen Luong Bang, Danang, Viet Nam
  • 3Tula State University, 92 Lenin Ave., Tula, Russia

Keywords: Adaptive model, Medical image denoising, Mixed noise, Total variation, Laplacian regularizer, Split Bregman

Abstract. Image denoising is one of the important tasks required by medical imaging analysis. In this work, we investigate an adaptive variation model for medical images restoration. In the proposed model, we have used the first-order total variation combined with Laplacian regularizer to eliminate the staircase effect in the first-order TV model while preserve edges of object in the piecewise constant image. We also propose an instance of Split Bregman method to solve the proposed denoising model as an optimization problem. Experimental results from mixed Poisson-Gaussian noise are given to demonstrate that our proposed approach outperforms the related methods.