# Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier arXiv: https://arxiv.org/abs/2405.16214 ยท HTML: https://mlsanity.com/paper/2405.16214/ ## Key facts - Cited by 7 papers in the map (data through 14 Oct 2026). - Placed in Inverse problems and image restoration, one of 14 directions in Diffusion models. - Builds on 30 of its 59 references that are in the map. - First posted on arXiv 25 May 2024. ## Placed in - Diffusion models / [Inverse problems and image restoration](https://mlsanity.com/diffusion/inverse-problems-and-image-restoration/) ## Builds on (most cited first, 30 of 30) - [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Classifier-Free Diffusion Guidance](https://mlsanity.com/paper/2207.12598/) (2022) - [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023) - [Generative Modeling by Estimating Gradients of the Data Distribution](https://mlsanity.com/paper/1907.05600/) (2019) - [Learning to Prompt for Vision-Language Models](https://mlsanity.com/paper/2109.01134/) (2021) - [An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion](https://mlsanity.com/paper/2208.01618/) (2022) - [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021) - [Image Super-Resolution via Iterative Refinement](https://mlsanity.com/paper/2104.07636/) (2021) - [RePaint: Inpainting using Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2201.09865/) (2022) - [Palette: Image-to-Image Diffusion Models](https://mlsanity.com/paper/2111.05826/) (2021) - [StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery](https://mlsanity.com/paper/2103.17249/) (2021) - [Blended Diffusion for Text-driven Editing of Natural Images](https://mlsanity.com/paper/2111.14818/) (2021) - [DiffusionCLIP: Text-Guided Diffusion Models for Robust Image Manipulation](https://mlsanity.com/paper/2110.02711/) (2021) - [ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2108.02938/) (2021) - [Understanding Diffusion Models: A Unified Perspective](https://mlsanity.com/paper/2208.11970/) (2022) - [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) - [Image Restoration with Mean-Reverting Stochastic Differential Equations](https://mlsanity.com/paper/2301.11699/) (2023) - [Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding](https://mlsanity.com/paper/2104.13015/) (2021) - [WaterGAN: Unsupervised Generative Network to Enable Real-time Color Correction of Monocular Underwater Images](https://mlsanity.com/paper/1702.07392/) (2017) - [Iterative Prompt Learning for Unsupervised Backlit Image Enhancement](https://mlsanity.com/paper/2303.17569/) (2023) - [Underwater Single Image Color Restoration Using Haze-Lines and a New Quantitative Dataset](https://mlsanity.com/paper/1811.01343/) (2018) - [UIEC^2-Net: CNN-based Underwater Image Enhancement Using Two Color Space](https://mlsanity.com/paper/2103.07138/) (2021) - [SGUIE-Net: Semantic Attention Guided Underwater Image Enhancement with Multi-Scale Perception](https://mlsanity.com/paper/2201.02832/) (2022) - [Underwater Image Enhancement by Transformer-based Diffusion Model with Non-uniform Sampling for Skip Strategy](https://mlsanity.com/paper/2309.03445/) (2023) - [UIEDP:Underwater Image Enhancement with Diffusion Prior](https://mlsanity.com/paper/2312.06240/) (2023) ## Built on by (most cited first, 7 of 7) - [Bayesian Neural Networks for One-to-Many Mapping in Image Enhancement](https://mlsanity.com/paper/2501.14265/) (2025) - [DPF-Net: Physical Imaging Model Embedded Data-Driven Underwater Image Enhancement](https://mlsanity.com/paper/2503.12470/) (2025) - [Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement](https://mlsanity.com/paper/2505.19895/) (2025) - [Closer to Ground Truth: Realistic Shape and Appearance Labeled Data Generation for Unsupervised Underwater Image Segmentation](https://mlsanity.com/paper/2503.16051/) (2025) - [Retinex Meets Language: A Physics-Semantics-Guided Underwater Image Enhancement Network](https://mlsanity.com/paper/2603.07076/) (2026) - [Empowering Semantic-Sensitive Underwater Image Enhancement with VLM](https://mlsanity.com/paper/2603.12773/) (2026) - [Two-Stage Teacher-Student Reliable Prior Learning for Robust Underwater Image Enhancement](https://mlsanity.com/paper/2608.00137/) (2026) ## Abstract (arXiv) Underwater Image Enhancement (UIE) aims to improve the visual quality from a low-quality input. Unlike other image enhancement tasks, underwater images suffer from the unavailability of real reference images. Although existing works exploit synthetic images and manually select well-enhanced images as reference images to train enhancement networks, their upper performance bound is limited by the reference domain. To address this challenge, we propose CLIP-UIE, a novel framework that leverages the potential of Contrastive Language-Image Pretraining (CLIP) for the UIE task. Specifically, we propose employing color transfer to yield synthetic images by degrading in-air natural images into corresponding underwater images, guided by the real underwater domain. This approach enables the diffusion model to capture the prior knowledge of mapping transitions from the underwater degradation domain to the real in-air natural domain. Still, fine-tuning the diffusion model for specific downstream tasks is inevitable and may result in the loss of this prior knowledge. To migrate this drawback, we combine the prior knowledge of the in-air natural domain with CLIP to train a CLIP-Classifier. Subsequently, we integrate this CLIP-Classifier with UIE benchmark datasets to jointly fine-tune the diffusion model, guiding the enhancement results towards the in-air natural domain. Additionally, for image enhancement tasks, we observe that both the image-to-image diffusion model and CLIP-Classifier primarily focus on the high-frequency region during fine-tuning. Therefore, we propose a new fine-tuning strategy that specifically targets the high-frequency region, which can be up to 10 times faster than traditional strategies. Extensive experiments demonstrate that our method exhibits a more natural appearance.