Underwater Image Enhancement based on Residual and Adversarial Network

Abstract

Due to the complexity and constraints of the underwater environment, underwa ter picture enhancement is a difficult task. In order to improve underwater images that have problems with low contrast, blurriness, and color mistakes, this research suggests a deep learning-based technique. Residual Networks (ResNet) and Super Resolution Generative Adversarial Networks (SRGANs) are combined in the sug gested method. In order to restore fine details and improve overall contrast and sharpness, ResNet extracts residual information. SRGANs produce enhanced under water picture versions at high resolution, enhancing visual integrity. Extensive testing on several underwater picture datasets reveals the suggested method’s superior performance. Comparing it to cutting-edge methods, objective quality indicators such as contrast augmentation, image sharpness, and color accu racy confirm its efficiency. Qualitative evaluations show that the underwater pho tographs have significantly improved in terms of contrast, blurriness, and color re production. This increases their ability to be analyzed and interpreted as well as their visual appeal. Marine research, underwater robots, and inspection systems can all benefit from better underwater image quality. Improved visual quality is bene ficial for accurate underwater object identification, biodiversity measurement, and extending our understanding of underwater ecosystems. In conclusion, this study provides a deep learning-based technique for enhancing underwater image quality that combines ResNet and SRGANs. The method addresses low contrast, blurriness, and color mistakes to produce notable improvements. Its effectiveness is supported by the experimental findings and qualitative evaluations, emphasizing its potential to advance underwater photography methods and applications