Underwater image enhancement method based on frequency domain analysis and visual Mama

Through a multi-stage wavelet transformation network based on frequency domain analysis and vision Mamba, the existing underwater image enhancement methods have solved the shortcomings in fast, few computing resources and high quality, and efficient underwater image enhancement is achieved, and the visual quality and computing efficiency of the image are improved.

CN119784598APending Publication Date: 2025-04-08SHANGHAI MARITIME UNIVERSITY
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Patent Information

Application Number
CN202411690682.3
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-11-25
Publication Date
2025-04-08

AI Technical Summary

Technical Problem

Existing underwater image enhancement methods have shortcomings in terms of fast, few computing resources and high quality, especially in the frequency domain.

Method used

Using a multi-stage wavelet transform network based on frequency domain analysis and visual Mamba, the high-frequency and low-frequency parts are separated by discrete wavelet transform, and the features are extracted using Mamba and convolution in the spatial-frequency feature fusion module, and the enhanced image is finally reconstructed by inverse wavelet transform.

Benefits of technology

It has achieved improvements in image visual quality and computational complexity, which can quickly and efficiently enhance underwater images, reduce computing resource requirements, and improve the details and structural recovery effect of the image.

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Abstract

The invention relates to an underwater image enhancement method based on frequency domain analysis and visual Mama, comprising the following steps: constructing a multistage wavelet transform network, the network being an encoder-decoder structure, the encoder comprising a space-frequency feature fusion module and discrete wavelet transform; the decoder comprises a space-frequency feature fusion module and inverse discrete wavelet transform; an underwater image is decomposed into a low-frequency part and a high-frequency part through discrete wavelet transform in an encoder, spatial feature extraction and frequency feature extraction are conducted on the low-frequency part and the high-frequency part through corresponding space-frequency feature fusion modules based on Mama and frequency domain convolution respectively, and splicing is conducted on the low-frequency part and the high-frequency part along a channel in the decoder according to the sequence of low frequency and high frequency. Reconstruction is carried out through inverse discrete wavelet transform; and inputting the underwater image data into the multistage wavelet transform network to obtain enhanced underwater image data. Compared with the prior art, the method has the advantages of improving the enhancement quality, reducing the complexity, quickly enhancing and the like.
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