Abstract
In the typical compressed signal recovery methods, the noise involved in the measurement process is assumed to be normal distributed. However, there might be impulsive noise existing in the real-world environment. Therefore, the assumption of normal distribution may lead to inaccurate recovery result. This paper proposes the Laplace density to model the measured noise of compressive sensing. A hierarchical Bayesian model is built for the model of the compressive sensing. The signal is assumed to be sparse under some transformation, and each coefficient of the transformation is supposed to be normal distributed with each precision being modeled by a gamma distribution. The zero coefficients can be automatically switched off via the proposed model setting. To estimate the parameters of the model, Variational Bayesian method is adopted to the proposed model. The proposed method is applied on the synthetic signal and the image signal; experimental results demonstrate the validity of the method.
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Wan, H., Zhang, H. (2020). Sparse Impulsive Noise Corrupted Compressed Signal Recovery Using Laplace Noise Density. In: Kountchev, R., Patnaik, S., Shi, J., Favorskaya, M. (eds) Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology. Smart Innovation, Systems and Technologies, vol 179. Springer, Singapore. https://doi.org/10.1007/978-981-15-3863-6_29
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DOI: https://doi.org/10.1007/978-981-15-3863-6_29
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Online ISBN: 978-981-15-3863-6
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