Multi-Level Perception Assessment for Underwater Image Enhancement

Due to the complex underwater imaging environment, existing Underwater Image Enhancement (UIE) techniques are unable to handle the increasing demand for high-quality underwater content in broadcasting systems. Thus, a robust quality assessment method is highly expected to effectively compare the quality of different enhanced underwater images. To this end, we propose a novel quality assessment method for enhanced underwater images by utilizing multiple levels of features at various stages of the network’s depth. We first select underwater images with different distortions to analyze the characteristics of different UIE results at various feature levels. We found that low-level features are more sensitive to color information, while mid-level features are more indicative of structural differences. Based on this, a Channel-Spatial-Pixel Attention Module (CSPAM) is designed for low-level perception to capture color characteristics, utilizing channel, spatial, and pixel dimensions. To capture structural variations, a Parallel Structural Perception Module (PSPM) with convolutional kernels of different scales is introduced for mid-level perception. For high-level perception, due to the accumulation of noise, an Adaptive Weighted Downsampling (AWD) layer is employed to restore the semantic information. Furthermore, a new top-down multi-level feature fusion method is designed. Information from different levels is integrated through a Selective Feature Fusion (SFF) mechanism, which produces semantically rich features and enhances the model’s feature representation capability. Experimental results demonstrate the superior performance of the proposed method over the competing image quality evaluation methods.