To address common issues in underwater images, such as color distortion and reduced contrast, as well as the limitations of supervised methods that rely on large-scale paired high-quality underwater image datasets, an unsupervised underwater image enhancement method is proposed. This method utilizes a conditional variational autoencoder (cVAE) combined with probabilistic adaptive instance normalization (PAdaIN) and multi-color space stretching techniques to improve the visual quality of generated images while ensuring consistency with the original input images. Furthermore, a multi-scale residual connection module is employed to effectively reduce the transmission of non-essential information, thereby enhancing the model's performance. This approach provides an alternative to traditional methods that rely on reference images for training.
Experimental results demonstrate that this method achieves a 12% and 3% improvement in Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) on the test set compared to FunieGAN and Water-Net, respectively, significantly enhancing the visual quality of the enhanced images. Moreover, the method exhibits excellent performance across different test sets, demonstrating its robust generalization capability. The study indicates that, without the need for reference images, this approach significantly improves underwater image quality, effectively enhancing image detail and color correction, and provides a viable solution for applications in aquaculture and marine monitoring.