Denoising method based on hybrid EMD (Empirical Mode Decomposition)

Abstract

The invention discloses a denoising method based on hybrid EMD (Empirical Mode Decomposition), and belongs to the technical field of digital signal processing. According to the denoising method disclosed by the invention, EEMD (Ensemble Empirical Mode Decomposition) and MEMD (Multivariate Empirical Mode Decomposition) algorithms are combined, and aiming at a masking signal frequency used in the MEMD algorithm, parameter optimization is carried out, so that mode mixing can be more effectively eliminated, and a denoising effect is better; and according to the denoising method, targeted optimization is further carried on an additive noise amplitude in the EEMD, filtering thresholds of noise related IMF (Spurious modes) and filtering thresholds of noise IMF, so that an integral denoising effect is further improved. Compared with the prior art, the method disclosed by the invention can more effectively eliminate mode mixing, and has a better denoising effect.

Description

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AI Technical Summary

Benefits of technology

This technology combines two algorithms: an enhanced version of EMMA with another one called MEE MD (Matrix Expansion Equation). These techniques help improve image quality while reducing unwanted artifacts such as moire fringe patterns or streaks on images captured under low light conditions. They also optimize certain factors like filter thresholds based upon their relative strength between different frequencies within each pixel's range. Overall these improvements enhance image clarity without compromisering important details about what they are supposedly doing well during imagery capturing at night time.

Problems solved by technology

This patents describes various technical techniques used during data analysis processes like machine learning or deep learning. These technologies involve analyzing complex datasets containing both numerical modes and symbolic patterns generated from inputted digital images. However, these algorithms also suffer from imperfections called end effects caused by factors other than just binary ones: start up errors due to unknown variables at specific points within the dataset. Existing solutions either require manual adjustment or use fixed thresholds, but they may result in improperly selected filters leading to modulation artifacts. Therefore there needs an improved methodology to reduce these issues while maintaining accuracy.

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