Underwater Image Enhancement Based on Attention Mechanism and Feature Reconstruction

To address the issues of existing underwater image enhancement methods, which lack focus on critical target objects in images and exhibit poor enhancement effects on edge detail information, in this study, an underwater image enhancement approach is proposed based on an attention mechanism and feature reconstruction. First, a superpixel image enhancement model is constructed by integrating the residual module with the Convolutional Block Attention Module (CBAM), which not only improves the overall quality of underwater images but also enhances the clarity and visibility of target objects in images. Second, an edge difference module is designed to enable the model to focus on high-frequency information in the images, thereby strengthening the edge details of the target objects. Finally, a multi-granularity feature reconstruction module is built to reconstruct the hidden layer features of the superpixel image enhancement model, restore the input image, and further optimize the model parameters. Experimental results demonstrate that when compared with contrastive methods, the proposed model realizes improvements in three evaluation metrics: Structural Similarity (SSIM), Peak Signal to Noise Ratio (PSNR), and Underwater Image Quality Measures (UIQM), indicating better enhancement performance. Notably, it exhibits a remarkable effect in enhancing critical target objects in underwater images.