Two-stage potential diffusion model underwater image enhancement method based on frequency domain

Through a two-stage potential diffusion model based on the frequency domain and combined with the prior knowledge of high and low frequency information, the problem that traditional underwater image enhancement methods fail to fully consider the degree of high and low frequency degradation is solved, and the recovery and enhancement effect of high-quality underwater images is achieved.

CN119648599APending Publication Date: 2025-03-18NINGBO UNIV
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Patent Information

Application Number
CN202411484416.5
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-10-23
Publication Date
2025-03-18

AI Technical Summary

Technical Problem

Traditional underwater image enhancement methods fail to fully consider the differences in the degree of partial degradation of high and low frequencies of underwater images, resulting in limited enhancement effects.

Method used

The underwater image enhancement method based on the frequency domain is adopted. Through the prior knowledge of high and low frequency information, it is divided into two stages: preliminary recovery and detailed recovery. The high and low frequency feature pyramid encoder and decoder are used to combine the latent diffusion model and the high and low frequency prior extraction module to generate high and high and low frequency a priori extraction module.

Benefits of technology

It effectively improves the performance of the potential diffusion model, restores high-quality underwater images, and significantly improves the visual fidelity and enhancement effect of underwater images.

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Abstract

The invention discloses a two-stage potential diffusion model underwater image enhancement method based on a frequency domain. The method comprises the following steps: inputting a degraded image and ambient light estimated according to the degraded image into a lightweight underwater image enhancement model to obtain a preliminary recovery image; inputting the preliminary recovery image into a high and low frequency feature pyramid encoder to obtain a potential spatial feature map set and an intermediate feature map set, and inputting the potential spatial feature map set and the intermediate feature map set into a high and low frequency prior extraction module to obtain high and low frequency images and high and low frequency image prior; inputting the low-frequency guidance priori and the potential spatial feature map set of the low-frequency image into a low-frequency guidance condition module to generate a priori guidance condition, and sending the priori guidance condition into a potential diffusion model to obtain a reconstructed potential spatial feature map set; and inputting the reconstructed potential spatial feature map set, the intermediate feature map set and the high and low frequency images into a high and low frequency feature pyramid decoder in prior to obtain an enhanced image. And improving the performance of the potential diffusion model and recovering a high-quality underwater image by using high and low frequency information priori knowledge of the underwater image.
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