Affiliations: [a] College of Science, Nanchang Institute of Technology, Nanchang, Jiangxi, China
| [b] Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang Institute of Technology, Nanchang, Jiangxi, China
Correspondence:
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Corresponding author. Mingxi Ma, College of Science, Nanchang Institute of Technology, Nanchang 330099, Jiangxi, China. E-mail: mingxima@126.com.
Abstract: Many recent studies have shown that Euler’s elastica regularization performs better than the famous total variation (TV) regularization on keeping image features in smooth regions during the process of denoising. In addition, an adaptive weighted matrix combined with total variation is a key technique for well restoring local features of image. Considering these two factors, in this paper, we propose an adaptive Euler’s elastica model for Poisson image restoration so as to well preserve both image features in smooth regions and local features of image. To solve this non-smooth and non-convex model efficiently, we design an alternating direction method of multipliers. Experiments on both natural and synthetic images illustrate the effectiveness and efficiency of the proposed method over the state-of-the-art methods in Poisson restoration and denoising, respectively. In particular, for Poisson restoration, our proposed method improves the TV method up to 2.46 about PSNR for dealing with the Peppers image with Gaussian blur and noise level σ = 1. In addition, the proposed method for Poisson denoising gets higher PSNR and SSIM values than the TAC method, while costing less CPU time.
Keywords: Poisson restoration, Euler’s elastica regularization, adaptive weighted matrix, alternating direction method of multipliers (ADMM)
