MVG-FD: Multi-Modal Visual Guidance and Feature Decomposition for Underwater Image Restoration

Underwater images are frequently affected by light absorption and scattering, which lead to color distortion, reduced contrast, and blurred details, significantly degrading overall image quality. Most underwater image restoration methods are confined to the pixel space of the raw modality, overlooking the important role of other modalities and different frequency-domain features. As a result, the representational capacity of deep learning models is not fully realized, affecting the generation of high-quality images. To address the above issues, we propose Multi-modal Visual Guidance and Feature Decomposition (MVG-FD) method for underwater image restoration. Specifically, we introduce Modality Visual Guidance (MVG) module, which integrates the complementary information provided by depth modality features into the raw features to guide the model in restoring the color of underwater images. Meanwhile, we design Feature Decomposition (FD) module, which utilizes Learnable Wavelet Decomposition (LWD) to decompose and extract the high-frequency bands of the raw features to help restore the texture details of the image. MVG-FD significantly improves PSNR and SSIM on existing datasets. The code is available at: https://github.com/zhangao668/MVG-FD.