As the light travels through the wavefront coding (WFC) system, the modulation transfer function(MTF) of the WFC system was very low, consequently the intermediate blurred image has been received by the detector. Howe...
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ISBN:
(纸本)9781510604599;9781510604605
As the light travels through the wavefront coding (WFC) system, the modulation transfer function(MTF) of the WFC system was very low, consequently the intermediate blurred image has been received by the detector. However, there is no zero point in the passband of the MTF of the WFC imaging system, and the target information cannot be saved very well. An appropriate filter can be used to restore the sampled intermediate image. The noise of the system is enlarged in the restoration process where the signal be amplified by the filter, and the signal to noise ratio(SNR) of the image is reduced. In order to solve the above issues, an improved algorithm has been proposed in this paper. The noise is controlled by the wavelet in the reconstruction process, and the intermediate blurred image is restored by the wiener filter algorithm with a prior knowledge of the degradation function. Thus, the wavelet de-noising and wiener filter algorithm are combined to restore the middle blurred image of the WFC system. Finally, the restoration image with the diffraction limit level is acquired in image detail restoration and noise control.
Speaker recognition is a major challenge in various languages for researchers. For programmed speaker recognition structure prepared by utilizing ordinary speech, shouting creates a confusion between the enlistment an...
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Speaker recognition is a major challenge in various languages for researchers. For programmed speaker recognition structure prepared by utilizing ordinary speech, shouting creates a confusion between the enlistment and test, henceforth minimizing the identification execution as extreme vocal exertion is required during shouting. Speaker recognition requires more time for classification of data, accuracy is optimized, and the low root-mean-square error rate is the major problem. The objective of this work is to develop an efficient system of speaker recognition. In this work, an improved method of wiener filter algorithm is applied for better noise reduction. To obtain the essential feature vector values, Mel-frequency cepstral coefficient feature extraction method is used on the noise-removed signals. Furthermore, input samples are created by using these extracted features after the dimensions have been reduced using probabilistic principal component analysis. Finally, recurrent neural network-bidirectional long-short-term memory is used for the classification to improve the prediction accuracy. For checking the effectiveness, the proposed work is compared with the existing methods based on accuracy, sensitivity, and error rate. The results obtained with the proposed method demonstrate an accuracy of 95.77%.
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