Underwater image restoration with UNet model

This abstract discusses methods and techniques for underwater image restoration. Underwater images are often affected by factors such as light scattering, color dispersion, and suspended particles, leading to blurriness, distortion, and difficulty in recognizing features. In order to improve the quality of underwater images, researchers have proposed restoration techniques based on mathematical models and computational methods. These include steps such as removal of scattered light, color correction, filtering, and contrast enhancement to enhance the clarity and realism of the images. Additionally, the application of deep learning techniques in underwater image restoration has shown significant progress. Through extensive training with large datasets, models can automatically learn and adapt to the restoration needs of different underwater environments. In summary, this study proposed a Vision Transformer-base UNet model for underwater image restoration which test on Large Scale Underwater Image(LSUI) dataset.