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This paper proposes a robust image restoration method using two-dimensional block Kalman filter with colored driving source. This method aims to achieve high quality image restoration for blur and noise disturbance from the canonical state space models with (i) a state equation composed of the original image, and (ii) an observation equation composed of the original image, blur, and noise. The remarkable...
We propose a noise suppression algorithm using Kalman filter with colored driving source. The algorithm aims to achieve robust noise suppression with reduced computational complexity by modifying the canonical state space models in. The remarkable features of the proposed algorithm are that it can be realized by 3 multiplications and that it has the better performances compared with despite the reduction...
We have proposed a robust noise suppression algorithm with Kalman filter theory. In this paper, we propose a Kalman filter based fast noise suppression algorithm for white and colored disturbance. The algorithm aims to achieve robust noise suppression with reduced computational complexity without sacrificing high quality of speech signal, by modifying the proposed canonical space model. We show the...
This paper deals with the problem of noise suppression for white and colored noises. Kalman filter based noise suppression is well known as effective approach, and usually performs the parameter estimation algorithm of AR (auto-regressive) system and then the Kalman filter algorithm. In this paper, we propose Kalman filter for robust noise suppression without the conception of AR system. The algorithm...
We propose a noise suppression algorithm based on Kaiman filter. The algorithm achieves a noise suppression with high speech quality under the condition of the AWGN (additive white Gaussian noise), from the canonical state space models with a state equation composed of the clean speech signal and an observation equation composed of the clean speech signal and AWGN. The special feature of the proposed...
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