A Robust Underwater Image Enhancement Algorithm

Capturing clear images in underwater environments is a major challenge in marine engineering. There are many issues to consider in obtaining clear underwater images. Such as climate, environment and human factors. The most important reasons are the atomization effect caused by dispersion and the color cast caused by inconsistent energy attenuation of each wavelength when light propagates in water. We propose a deep learning model for inferring a degradation model to further improve image dynamic range through a network-guided underwater image enhancement network architecture with multicolor space embedding and convolutional media transfer. Fixed an issue with limited dynamic range and brightness in underwater images. Quantitative and qualitative results show that our network performs relatively well in the Underwater Image Enhancement Benchmark (UIEB) dataset compared to other recent methods, and is expected to be applied to different types of underwater work and environments in the future. Image enhancement. And reduce the degradation problems that often occur with underwater images.