Underwater image enhancement method and system based on latent space diffusion and adaptive style transfer

Abstract

This invention belongs to the field of vision and image processing technology, specifically providing an underwater image enhancement method and system based on latent space diffusion and adaptive style transfer. First, underwater and non-underwater image datasets are constructed and preprocessed using standardization. A pre-trained CNN is used to extract features from non-underwater images at multiple levels, calculating the Gram matrix of the convolutional layers as style features. The underwater images are input into a variational encoder, mapped to a low-dimensional latent space to obtain feature vectors. A diffusion denoising network predicts and removes noise in the latent space. An adaptive style transfer network combines the style features of the non-underwater images to achieve decoupling and fusion of content and style. Finally, the enhanced underwater image is reconstructed by a variational decoder. This invention solves the problems of existing methods relying on paired data, high computational complexity, and lacking an effective decoupling mechanism, achieving efficient underwater image color correction and sharpness enhancement.

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