HIFI-Net: A Novel Network for Enhancement to Underwater Images
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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.
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.
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References · 8
- 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,694
- 2018CBAM: Convolutional Block Attention Modulecited by 2,477
- 2019An Underwater Image Enhancement Benchmark Dataset and Beyondcited by 253
- 2019Fast Underwater Image Enhancement for Improved Visual Perceptioncited by 183
- 2017Fast and Accurate Image Super-Resolution with Deep Laplacian Pyramid Networkscited by 138
- 2020Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perceptioncited by 53
- 2017Emerging from Water: Underwater Image Color Correction Based on Weakly Supervised Color Transfercited by 41
- 2021Shallow-UWnet : Compressed Model for Underwater Image Enhancementcited by 35
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