# Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration

arXiv: https://arxiv.org/abs/2311.16845 · HTML: https://mlsanity.com/paper/2311.16845/

## Key facts

- Cited by 88 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 51 references that are in the map.
- First posted on arXiv 28 Nov 2023.

## Builds on (most cited first, 28 of 28)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021)
- [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021)
- [Zero-Shot Image Restoration Using Denoising Diffusion Null-Space Model](https://mlsanity.com/paper/2212.00490/) (2022)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [Deblurring via Stochastic Refinement](https://mlsanity.com/paper/2112.02475/) (2021)
- [Fast Underwater Image Enhancement for Improved Visual Perception](https://mlsanity.com/paper/1903.09766/) (2019)
- [TF-ICON: Diffusion-Based Training-Free Cross-Domain Image Composition](https://mlsanity.com/paper/2307.12493/) (2023)
- [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021)
- [Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Model](https://mlsanity.com/paper/2308.13164/) (2023)
- [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017)
- [Enhancing Underwater Imagery using Generative Adversarial Networks](https://mlsanity.com/paper/1801.04011/) (2018)
- [Contrastive Semi-supervised Learning for Underwater Image Restoration via Reliable Bank](https://mlsanity.com/paper/2303.09101/) (2023)
- [Pyramid Diffusion Models For Low-light Image Enhancement](https://mlsanity.com/paper/2305.10028/) (2023)
- [DocDiff: Document Enhancement via Residual Diffusion Models](https://mlsanity.com/paper/2305.03892/) (2023)
- [UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space](https://mlsanity.com/paper/2103.07138/) (2021)
- [Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategy](https://mlsanity.com/paper/2309.03445/) (2023)
- [All-In-One Underwater Image Enhancement using Domain-Adversarial Learning](https://mlsanity.com/paper/1905.13342/) (2019)
- [Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement](https://mlsanity.com/paper/2309.04089/) (2023)
- [Deep Underwater Image Enhancement](https://mlsanity.com/paper/1807.03528/) (2018)
- [A Wavelet-based Dual-stream Network for Underwater Image Enhancement](https://mlsanity.com/paper/2202.08758/) (2022)
- [Underwater Image Enhancement via Learning Water Type Desensitized Representations](https://mlsanity.com/paper/2102.00676/) (2021)
- [Real-Time Multi-Diver Tracking and Re-identification for Underwater Human-Robot Collaboration](https://mlsanity.com/paper/1910.09636/) (2019)

## Built on by (most cited first, 30 of 88)

- [MACE: Mass Concept Erasure in Diffusion Models](https://mlsanity.com/paper/2403.06135/) (2024)
- [MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis](https://mlsanity.com/paper/2402.05408/) (2024)
- [Robust Watermarking Using Generative Priors Against Image Editing: From Benchmarking to Advances](https://mlsanity.com/paper/2410.18775/) (2024)
- [Set You Straight: Auto-Steering Denoising Trajectories to Sidestep Unwanted Concepts](https://mlsanity.com/paper/2504.12782/) (2025)
- [STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution](https://mlsanity.com/paper/2501.02976/) (2025)
- [DiP: Taming Diffusion Models in Pixel Space](https://mlsanity.com/paper/2511.18822/) (2025)
- [3DIS: Depth-Driven Decoupled Instance Synthesis for Text-to-Image Generation](https://mlsanity.com/paper/2410.12669/) (2024)
- [MIGC++: Advanced Multi-Instance Generation Controller for Image Synthesis](https://mlsanity.com/paper/2407.02329/) (2024)
- [Does FLUX Already Know How to Perform Physically Plausible Image Composition?](https://mlsanity.com/paper/2509.21278/) (2025)
- [Conditional Image Synthesis with Diffusion Models: A Survey](https://mlsanity.com/paper/2409.19365/) (2024)
- [DragFlow: Unleashing DiT Priors with Region Based Supervision for Drag Editing](https://mlsanity.com/paper/2510.02253/) (2025)
- [DreamRenderer: Taming Multi-Instance Attribute Control in Large-Scale Text-to-Image Models](https://mlsanity.com/paper/2503.12885/) (2025)
- [TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation](https://mlsanity.com/paper/2408.17135/) (2024)
- [Visual-Instructed Degradation Diffusion for All-in-One Image Restoration](https://mlsanity.com/paper/2506.16960/) (2025)
- [Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement](https://mlsanity.com/paper/2403.01497/) (2024)
- [Investigating Text Insulation and Attention Mechanisms for Complex Visual Text Generation](https://mlsanity.com/paper/2503.23461/) (2025)
- [From Zero to Detail: Deconstructing Ultra-High-Definition Image Restoration from Progressive Spectral Perspective](https://mlsanity.com/paper/2503.13165/) (2025)
- [UltraHR-100K: Enhancing UHR Image Synthesis with A Large-Scale High-Quality Dataset](https://mlsanity.com/paper/2510.20661/) (2025)
- [LUVE : Latent-Cascaded Ultra-High-Resolution Video Generation with Dual Frequency Experts](https://mlsanity.com/paper/2602.11564/) (2026)
- [WaveMamba: Wavelet-Driven Mamba Fusion for RGB-Infrared Object Detection](https://mlsanity.com/paper/2507.18173/) (2025)
- [RayFlow: Instance-Aware Diffusion Acceleration via Adaptive Flow Trajectories](https://mlsanity.com/paper/2503.07699/) (2025)
- [MobileIE: An Extremely Lightweight and Effective ConvNet for Real-Time Image Enhancement on Mobile Devices](https://mlsanity.com/paper/2507.01838/) (2025)
- [Dual Frequency Branch Framework with Reconstructed Sliding Windows Attention for AI-Generated Image Detection](https://mlsanity.com/paper/2501.15253/) (2025)
- [Fourier-enhanced Implicit Neural Fusion Network for Multispectral and Hyperspectral Image Fusion](https://mlsanity.com/paper/2404.15174/) (2024)
- [BideDPO: Conditional Image Generation with Simultaneous Text and Condition Alignment](https://mlsanity.com/paper/2511.19268/) (2025)
- [A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning](https://mlsanity.com/paper/2405.19684/) (2024)
- [O-Mamba: O-shape State-Space Model for Underwater Image Enhancement](https://mlsanity.com/paper/2408.12816/) (2024)
- [L2P: Unlocking Latent Potential for Pixel Generation](https://mlsanity.com/paper/2605.12013/) (2026)
- [Plenodium: UnderWater 3D Scene Reconstruction with Plenoptic Medium Representation](https://mlsanity.com/paper/2505.21258/) (2025)
- [Latent Wavelet Diffusion For Ultra-High-Resolution Image Synthesis](https://mlsanity.com/paper/2506.00433/) (2025)

## Abstract (arXiv)

Underwater images are subject to intricate and diverse degradation, inevitably affecting the effectiveness of underwater visual tasks. However, most approaches primarily operate in the raw pixel space of images, which limits the exploration of the frequency characteristics of underwater images, leading to an inadequate utilization of deep models' representational capabilities in producing high-quality images. In this paper, we introduce a novel Underwater Image Enhancement (UIE) framework, named WF-Diff, designed to fully leverage the characteristics of frequency domain information and diffusion models. WF-Diff consists of two detachable networks: Wavelet-based Fourier information interaction network (WFI2-net) and Frequency Residual Diffusion Adjustment Module (FRDAM). With our full exploration of the frequency domain information, WFI2-net aims to achieve preliminary enhancement of frequency information in the wavelet space. Our proposed FRDAM can further refine the high- and low-frequency information of the initial enhanced images, which can be viewed as a plug-and-play universal module to adjust the detail of the underwater images. With the above techniques, our algorithm can show SOTA performance on real-world underwater image datasets, and achieves competitive performance in visual quality.