A critical analysis of the multi-focus image fusion using discrete wavelet transform and computer vision

Gebremeskel, Gebeyehu Belay

doi:10.1007/s00500-022-06998-w

Cited by 11 publications

(1 citation statement)

References 39 publications

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“…In 1974, the mathematician Meyer proved the existence of wavelet function and carried out an in-depth study in the theory. So far, wavelet transform theory has been widely used in signal analysis [ 16 ], image processing [ 17 ], fault diagnosis [ 18 ], and other engineering fields.…”

Section: Related Workmentioning

confidence: 99%

Text Feature Extraction for Public English Vocabulary Based on Wavelet Transform

Shi

2022

Computational and Mathematical Methods in Medicine

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Text interpretation of public English vocabulary is a critical task in the subject of natural language processing, which uses technology to allow humans and computers to communicate effectively using natural language. Text feature extraction is one of the most fundamental and crucial elements in allowing computers to effectively grasp and read text. This paper proposes a text feature extraction method based on wavelet analysis that performs fast discrete wavelet transform and inverse discrete wavelet transform on the feature vectors under the traditional TF-IDF vector space model to address the problem of low feature differentiation of high-dimensional data in text feature extraction. In particular, due to the design of the Mallat algorithm, there is frequency aliasing in the signal decomposition process. This phenomenon is a problem that cannot be ignored when using wavelet analysis for feature extraction. Therefore, this paper proposes an improved inverse discrete wavelet transform method, in which the signal is decomposed by Mallat algorithm to obtain wavelet coefficients at each scale and then reconstructed to the required wavelet space coefficients according to the reconstruction method, and the reconstructed coefficients are used to analyze the signal at that scale instead of the wavelet coefficients obtained at the corresponding scale. Experiments on the public English vocabulary dataset reveal that the wavelet transform-based strategy suggested in this research outperforms existing feature extraction methods while maintaining greater classification accuracy while reducing the dimensionality of the TF-IDF vector space model.

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Section: Related Workmentioning

confidence: 99%