A multi-level wavelet-based underwater image enhancement network with color compensation prior

Due to the scattering of light and the influence of different water types, underwater images usually suffer from different type of hybrid degradation, e.g. color distortion, blurred details and low contrast. Existing underwater image enhancement methods are weak at handling hybrid degradation simultaneously, resulting in low quality results. Inspired by the fact that wavelet-based enhancement methods can correct color and enhance details in frequency domain and the color compensation prior can compensate missing color information in spatial domain, we design the Multi-level Wavelet-based Underwater Image Enhancement Network (MWEN) with the color compensation prior to enhance image in both frequency domain and spatial domain. Specifically, we integrate the multi-level wavelet transform and the color compensation prior into a multi-stage enhancement framework, where each stage consists of a Multi-level Wavelet-based Enhancement Module (MWEM), a Color Compensation Prior Extraction Module (CCPEM) and a color filter with prior-aware weights. The MWEM decomposes image features into low frequency and high frequency by a wavelet transform, and then enhances them by a low frequency enhancement branch and several high frequency enhancement branches, respectively. The low frequency reduces the color distortion of different water types using Instance Normalization for style transfer, while the high frequency enhancement enhances sparse details using a non-local sparse attention mechanism. After the inverse wavelet transform, the preliminary enhanced result by the MWEM is obtained. Then, the color filter whose weights are customized by the color compensation information extracted from the CCPEM dynamically is applied to output of the MWEM for color compensation. Such an operation enables network to adapt to hybrid degradation and achieve better performance. The experiments demonstrate MWEN outperforms existing UIE methods quantitatively and qualitatively.