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A novel image denoising algorithm using linear Bayesian MAP estimation based on sparse representation
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DOI：10.1016/j.sigpro.2014.01.022.png)
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Abstract
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A novel image denoising algorithm using linear Bayesian maximum a posteriori (MAP) estimation based on sparse representation model is proposed. Starting from constructing prior probability distribution in representation vector, a linear Bayesian MAP estimator is constructed in order to acquire the most probable one behind the observations, which is adaptive to solve the generalized image inverse problems. Furthermore, a practical closedform solution by affording some plausible approximations is obtained, and thus image denoising as a specialization can be easily solved. With our new method, we first extract all possible patches from noisy images and classify them to several sub-groups by their structural patterns, then train a different dictionary per each using the K-SVD algorithm, following by estimating corresponding parameters in MAP estimator. The final denoised image is obtained by applying denoising on each sub-group based on the estimator and averaging these outputs together. Simulated results show that the proposed method achieves a very competitive performance both in subjective visual quality and objective PSNR value, compared with other state-of-the-art denoising algorithms. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Image denoising
Linear Bayesian estimation
Sparse representation
Orthonormal matching pursuit
K-SVD algorithm
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