UResNet-Based Enhancement of Underwater Images through Variational Contrast and Saturation

Underwater imaging is an ever trending and evolving field in which the images obtained are subjected to enhancement to identify the critical features of the underwater world. Various enhancement approaches have evolved and certain issues have been identified in these techniques leading to a new technique. Generally, underwater images suffer from blurriness, color distortion, light absorption and scattering in water, low contrast issues which affect the overall clarity and visual appeal of the image. Our research demonstrates that hybrid preprocessing when combined with advanced deep learning models such as Underwater ResNet (UResNet) give high quality and visually appealing images. Three model variants have been explored in this work, the standard UResNet, UResNet augmented with a Sobel filter, and UResNet enhanced with a squeeze-and-excitation (SE) block. The models have employed on the EUVP (Enhancing Underwater Visual Perception) dataset. The core challenges such as color distortion, low contrast, and poor saturation have been overcome by the hybrid preprocessing pipeline, which combines Underwater White Balance (UWB) and Variational Contrast and Saturation Enhancement (VCSE) techniques that corrects the color imbalances, enhances the contrast, and amplifies the saturation before deep learning interference. The pre-processed images were given to all modified UResNet models and it was found that all models outperform the baseline model in both qualitative and quantitative evaluations. Among the three models that were modified, the SE-integrated model consistently delivered superior performance with all the metrics, highlighting significant improvements in color fidelity, contrast, edge definition, and overall image clarity. These results show the effectiveness of combining hybrid preprocessing with structure-aware deep learning architectures to improve underwater image quality and adaptability across diverse marine environments.