HIFI-Net: A Novel Network for Enhancement to Underwater Images
This work addresses the problem of improving visual quality in underwater images, which is important for applications like marine exploration, but it appears incremental as it builds on existing enhancement methods with a novel fusion approach.
The authors tackled underwater image enhancement by proposing HIFI-Net, a network that uses a Reinforcement Fusion Module for Haar wavelet images to fuse original images with important information, achieving state-of-the-art performance on three datasets across multiple metrics.
A novel network for enhancement to underwater images is proposed in this paper. It contains a Reinforcement Fusion Module for Haar wavelet images (RFM-Haar) based on Reinforcement Fusion Unit (RFU), which is used to fuse an original image and some important information within it. Fusion is achieved for better enhancement. As this network make "Haar Images into Fusion Images", it is called HIFI-Net. The experimental results show the proposed HIFI-Net performs best among many state-of-the-art methods on three datasets at three normal metrics and a new metric.