MPNet: Multiscale Compensated Probabilistic Adaptive Style Transfer Network for Underwater Image Enhancement

Underwater images often suffer from color distortion and low contrast due to complex degradation factors, severely limiting their utility in many fields, such as marine exploration. Although existing methods predominantly focus on establishing a deterministic mapping from the degraded images to the enhanced images, they frequently overlook underwater environmental diversity in water types and lighting conditions. Some studies have noticed this problem, but the proposed methods often cause overcorrection of image colors and low contrast. To address these limitations, we propose the MPNet, a novel deep learning network capable of restoring color details and improving contrast in underwater images. Specifically, our approach introduces a novel framework centered around a Probabilistic Adaptive Style Transfer PAST module that integrates depthwise separable convolutions for uncertainty-aware enhancement to achieve more generalized color correction and contrast enhancement. Furthermore, a Multiscale Color-Texture Compensation MCTC module is developed through texture-color feedback utilizing parameter-shared SE-Res blocks and cross-layer fusion to mitigate detail loss and color bias in deep networks. Extensive experiments on the UIEB and the EUVP datasets demonstrate improvements in superiority over other advanced methods. Qualitative and visual results confirm its ability to effectively restore the color texture details and enhance contrast.