# HIFI-Net: A Novel Network for Enhancement to Underwater Images

arXiv: https://arxiv.org/abs/2206.02295 · HTML: https://mlsanity.com/paper/2206.02295/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 6 Jun 2022.

## Builds on (most cited first, 8 of 8)

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networks](https://mlsanity.com/paper/1710.01992/) (2017)
- [Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception](https://mlsanity.com/paper/2002.01155/) (2020)
- [Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfer](https://mlsanity.com/paper/1710.07084/) (2017)
- [Shallow-UWnet : Compressed Model for Underwater Image Enhancement](https://mlsanity.com/paper/2101.02073/) (2021)

## Abstract (arXiv)

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.