Dual-Branch Wavelet Diffusion models with Dual-Prior Refinement for Underwater Image Enhancement

Underwater images often suffer from color distortion and detail loss due to the scattering and absorption of light, presenting significant challenges in Underwater Image Enhancement (UIE). Although wavelet-based learning methods address this problem by correcting colors in low-frequency components and enhancing details in high-frequency components, they still struggle to achieve visual fidelity for human perception. As a perceptually driven approach, conditional Denoising Diffusion Models (CDDMs) combined with wavelet transforms have been widely adopted for UIE. However, these methods often focus on the generative capability of CDDM in the low-frequency components, while neglecting the effectiveness of CDDM in high-frequency processing as well as the role of accurate priors in guiding the diffusion process. To address these limitations, we propose Dual-Branch Wavelet Diffusion models with Dual-Prior Refinement (DwaveDiff) for UIE. By decomposing the image into low-frequency and high-frequency subbands using the Haar wavelet transform, the reduced-dimensional frequency information not only accelerates CDDM inference but also provides distinct subbands, allowing CDDM to effectively handle color correction and detail recovery separately. Specifically, we use the Red Channel Prior image as a condition for the low-frequency branch of the CDDM to correct color, and the Edge Captured Map as a condition for the high-frequency branch of the CDDM to recover details. In addtion, the prior refinement strategy in the CDDM ensures that accurate prior information is used, guiding DwaveDiff to perform effective enhancement. Experimental results on both synthetic and real-world image datasets demonstrate that our method outperforms existing approaches both quantitatively and qualitatively.