基于图像块先验的低秩近似和维纳滤波的去噪算法

doi:10.6040/j.issn.1672-3961.0.2017.009

Abstract

Abstract: A Gaussian mixture model(GMM)was used to study the texture structure of natural image patches, and a low-rank approximation and Wiener filtering algorithm based on image patch prior were proposed. The proposed method divided the image into a number of overlapped patches and clustered them for collaborative filtering by using the prior structures of external image patch and internal image self-similarity. By grouping nonlocal similar patches, low-rank approximation was used as collaborative filtering to recover the texture structures. When the number of similar patches was small, Wiener filtering with patch prior was adopted to preserve texture features. The experimental results indicated that the proposed method was more suitable for the images with fewer similar patches like boundary and corner etc., and showed very competitive performance with state-of-the-art denoising method in terms of Peak Signal to Noise Ratio(PSNR)and visual quality.

Key words: low-rank approximation, prior, Wiener filtering, Gaussia mixture model

CLC Number:

TP37

ZHANG Yang, CHEN Fei, XU Haiping. Image patch prior based denoising algorithm by using low rank approximation and Wiener filtering[J].JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2017, 47(3): 16-20.

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Image patch prior based denoising algorithm by using low rank approximation and Wiener filtering

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