Gaussian patch mixture model guided low-rank covariance matrix minimization for image denoising. (English) Zbl 1524.62474
Summary: Image denoising is one of the most important tasks in image processing. In this paper, we study image denoising methods by using similar patches which have low-rank covariance matrices to recover an underlying image which is corrupted by additive Gaussian noise. In order to enhance global patch-matching results, we make use of a Gaussian mixture model with an auxiliary image to determine different groups of patches. The auxiliary image is an output of BM3D. The noisy version of covariance matrix is formed by each group of patches from the given noisy image. Its low-rank version can be estimated by using covariance matrix nuclear norm minimization, and the resulting denoised image can be obtained. Experimental results are reported to show that the proposed method outperforms the state-of-the-art denoising methods, including testing deep learning methods, in the peak signal-to-noise ratio, structural similarity values, and visual quality.
MSC:
| 62M40 | Random fields; image analysis |
| 65D18 | Numerical aspects of computer graphics, image analysis, and computational geometry |
| 94A08 | Image processing (compression, reconstruction, etc.) in information and communication theory |
Keywords:
image denoising; Gaussian mixture model; covariance matrix; nuclear norm; low-rank matrix factorizationReferences:
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