A Time-Frequency Image Denoising Method Based on Time-Frequency Matrix

Abstract

The invention belongs to the technical field of time-frequency image denoising, and in particular relates to a time-frequency matrix-based time-frequency image denoising method. The present invention arranges the entire time-frequency matrix in ascending order from small to large, takes the mean value of the first 20% of the data, and takes the maximum value of the entire time-frequency matrix, supplemented by the global maximum value and minimum value for correction, and finally The three are weighted and averaged in a ratio of 1:2:1 to obtain a dynamic threshold calculation method. The present invention uses the weighted average of the global maximum value, the mean value of the first 20% of the time-frequency matrix and the modified mean value as the interception threshold of the time-frequency image denoising. The point detection rate is higher than energy threshold denoising and histogram denoising. And, generally speaking, the performance of the improved denoising threshold proposed by this method is relatively stable.

Description

technical field [0001] The invention belongs to the technical field of time-frequency graph denoising, and in particular relates to a time-frequency graph denoising method based on a time-frequency matrix. Background technique [0002] The modern communication environment becomes more and more severe with the development of technology, in which noise is superimposed on single or multiple frequency hopping signals, which makes the extraction, sorting and parameter estimation of frequency hopping signals difficult. In order to make the following processing effects such as parameter estimation of the frequency hopping signal better, it is necessary to extract a purer frequency hopping signal in a complex environment, that is, it is necessary to remove noise without causing interference and energy loss to the useful frequency hopping signal. In the context of time-frequency analysis, it is first necessary to set a truncation threshold to denoise the obtained time-frequency graph...

AI Technical Summary

Problems solved by technology

[0003] Compared with the preset fixed threshold, the threshold values ​​of the energy threshold denoising algorithm and the histogram denoising algorithm are adaptive truncation thresholds that change with the change of the signal-to-noise ratio, and the performance of the two algorithms is better. But the complexity is high, and the turning point in the energy threshold denoising algorithm is not easy to locate accurately in discrete signal processing, and the distinction point between noise and signal energy distribution in the histogram denoising algorithm is also not easy to accurately locate, so this Both dynamic thresholds have certain limitations in theory and engineering practice

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