Comparative evaluations of various deconvolution methods reveal that with the ClassBD filters, the combination of time and frequency domain techniques outperforms either filter used in isolation.
Our experimental setup across multiple datasets demonstrates the dataset-dependence of filter efficiency, showing that T-filters can be more effective on certain datasets, while F-filters excel on others.
#blind-deconvolution #quadratic-neural-networks #filter-performance #machine-learning #computational-experiments
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