Fusion-UWnet: Multi-channel Fusion-based Deep CNN for Underwater Image Enhancement

Abstract

Underwater image enhancement has been considered as one of the prime research areas due to its massive significance in underwater surveillance and the development of underwater autonomous robotics. Deep learning methods have been used for image processing, where heavy models like GANs and very deep CNNs are being deployed for the task. Due to the bulky nature of the models, they consume significant memory and are numerically expensive in computational tasks, making them inefficient to some degree in underwater exploration tasks. These models are primarily trained over synthetically generated data which makes them less correlative for real-world tasks. This paper proposes a deep network architecture that uses a series of convolutional blocks to fuse significant complementary features of two separate enhanced versions of the input image along with the input one. Further, a combination of perceptual and structural similarity losses is used to find out the error. We have also benchmarked our model on three underwater datasets, highlighting the generalizing capabilities over a mix of real-world and synthetic data.