FloodNet: Underwater image restoration based on residual dense learning

Shankar Gangisetty, Raghu Raj Rai

Abstract: Highlights•Propose FloodNet, an end-to-end CNN architecture to restored underwater images from a wide variety of degraded underwater images.•Introduce residual dense blocks to connect the convolutional layers using skip-connections.•Global feature fusion to adaptively utilize both local and global residual learning.•Exhaustive perspective and quantitative experimental analysis on paired and unpaired underwater images.•Perform application and user study analysis.