The proposed framework integrates two blind deconvolution (BD) filters: a time domain quadratic convolutional filter and a frequency domain linear filter, optimizing input signal recovery.
The time domain filter employs a 16-channel quadratic convolutional neural network, effectively filtering and recovering input signals while ensuring output dimensions match inputs, enhancing efficiency.
Utilizing fast Fourier transform, the frequency domain filter emphasizes discrete frequency components, applying a linear neural layer to optimize the filtering process toward signal recovery.
An envelope spectrum objective function is developed to guide optimization, reinforcing the framework’s capability of addressing noise and ensuring the integrity of extracted features.
#blind-deconvolution #quadratic-neural-networks #signal-processing #machine-learning #feature-extraction
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