Hybrid image restoration model with adaptive weight parameter

Jimin Yu; Xingtuo Zhang; Shangbo Zhou

doi:10.1117/1.JEI.26.5.053007

9 September 2017 Hybrid image restoration model with adaptive weight parameter

Jimin Yu, Xingtuo Zhang, Shangbo Zhou

Author Affiliations +

Funded by: Major Project of Fundamental Science and Frontier Technology Research of Chongqing CSTC

Journal of Electronic Imaging, Vol. 26, Issue 5, 053007 (September 2017). https://doi.org/10.1117/1.JEI.26.5.053007

Abstract

It is a difficult issue in image restoration to eliminate noise while avoiding the staircase effect and preserving edges. The anisotropic diffusion model proposed by Perona and Malik (PM) and the total variation model presented by Rudin, Osher, and Fatemi (ROF) are widely used to restore an image. However, the well-known defect of the two classic models is that they tend to cause the staircase effect. We propose a well-balanced anisotropic diffusion (WBAD) model by considering an adaptive balance parameter. The balance can be made in a selective way, meaning that it will alternate between the PM diffusion and ROF diffusion in accordance with the image features. The proposed WBAD model can preserve edges well while reducing noise, but it also causes less staircasing effect in the less smooth regions because it acts like the PM diffusion in these regions. Considering that the fourth-order PDEs can reduce the staircasing effect, we introduce a hybrid image restoration model based on an adaptive weight parameter to take advantage of the WBAD model and the fourth-order model. The experimental results illustrate that our algorithm can effectively remove noise and preserve edges. The higher values of peak signal-to-noise ratio and MSSIM highlight the better performance of our hybrid image restoration model.

Citation Download Citation

Jimin Yu, Xingtuo Zhang, and Shangbo Zhou "Hybrid image restoration model with adaptive weight parameter," Journal of Electronic Imaging 26(5), 053007 (9 September 2017). https://doi.org/10.1117/1.JEI.26.5.053007

Received: 12 May 2017; Accepted: 11 August 2017; Published: 9 September 2017

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