Exploiting Diffusion Prior for Real-World Image Dehazing with Unpaired Training

• URL: http://arxiv.org/abs/2503.15017v1
• Date: Wed, 19 Mar 2025 09:13:06 GMT
• Title: Exploiting Diffusion Prior for Real-World Image Dehazing with Unpaired Training
• Authors: Yunwei Lan, Zhigao Cui, Chang Liu, Jialun Peng, Nian Wang, Xin Luo, Dong Liu,
• Abstract summary: Unpaired training is one of the most effective paradigms for real scene dehazing by learning from hazy and clear images.<n>Inspired by the strong generative capabilities of diffusion models in producing both hazy and clear images, we exploit diffusion prior for real-world image dehazing.<n>We introduce a new perspective for adequately leveraging the representation ability of diffusion models by removing degradation in image and text modalities.
• Score: 11.902218695900217
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unpaired training has been verified as one of the most effective paradigms for real scene dehazing by learning from unpaired real-world hazy and clear images. Although numerous studies have been proposed, current methods demonstrate limited generalization for various real scenes due to limited feature representation and insufficient use of real-world prior. Inspired by the strong generative capabilities of diffusion models in producing both hazy and clear images, we exploit diffusion prior for real-world image dehazing, and propose an unpaired framework named Diff-Dehazer. Specifically, we leverage diffusion prior as bijective mapping learners within the CycleGAN, a classic unpaired learning framework. Considering that physical priors contain pivotal statistics information of real-world data, we further excavate real-world knowledge by integrating physical priors into our framework. Furthermore, we introduce a new perspective for adequately leveraging the representation ability of diffusion models by removing degradation in image and text modalities, so as to improve the dehazing effect. Extensive experiments on multiple real-world datasets demonstrate the superior performance of our method. Our code https://github.com/ywxjm/Diff-Dehazer.

Related papers

• Learning Hazing to Dehazing: Towards Realistic Haze Generation for Real-World Image Dehazing [59.43187521828543]
  We introduce a novel hazing-dehazing pipeline consisting of a Realistic Hazy Image Generation framework (HazeGen) and a Diffusion-based Dehazing framework (DiffDehaze) HazeGen harnesses robust generative diffusion priors of real-world hazy images embedded in a pre-trained text-to-image diffusion model. By employing specialized hybrid training and blended sampling strategies, HazeGen produces realistic and diverse hazy images as high-quality training data for DiffDehaze.
  arXiv  Detail & Related papers  (2025-03-25T01:55:39Z)
• Disentangled World Models: Learning to Transfer Semantic Knowledge from Distracting Videos for Reinforcement Learning [93.58897637077001]
  This paper tries to learn and understand underlying semantic variations from distracting videos via offline-to-online latent distillation and flexible disentanglement constraints.<n>We pretrain the action-free video prediction model offline with disentanglement regularization to extract semantic knowledge from distracting videos.<n>For finetuning in the online environment, we exploit the knowledge from the pretrained model and introduce a disentanglement constraint to the world model.
  arXiv  Detail & Related papers  (2025-03-11T13:50:22Z)
• Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion Prior [63.64088590653005]
  We propose Diff-Mosaic, a data augmentation method based on the diffusion model. We introduce an enhancement network called Pixel-Prior, which generates highly coordinated and realistic Mosaic images. In the second stage, we propose an image enhancement strategy named Diff-Prior. This strategy utilizes diffusion priors to model images in the real-world scene.
  arXiv  Detail & Related papers  (2024-06-02T06:23:05Z)
• What Matters When Repurposing Diffusion Models for General Dense Perception Tasks? [49.84679952948808]
  Recent works show promising results by simply fine-tuning T2I diffusion models for dense perception tasks.<n>We conduct a thorough investigation into critical factors that affect transfer efficiency and performance when using diffusion priors.<n>Our work culminates in the development of GenPercept, an effective deterministic one-step fine-tuning paradigm tailed for dense visual perception tasks.
  arXiv  Detail & Related papers  (2024-03-10T04:23:24Z)
• Intrinsic Image Diffusion for Indoor Single-view Material Estimation [55.276815106443976]
  We present Intrinsic Image Diffusion, a generative model for appearance decomposition of indoor scenes. Given a single input view, we sample multiple possible material explanations represented as albedo, roughness, and metallic maps. Our method produces significantly sharper, more consistent, and more detailed materials, outperforming state-of-the-art methods by $1.5dB$ on PSNR and by $45%$ better FID score on albedo prediction.
  arXiv  Detail & Related papers  (2023-12-19T15:56:19Z)
• Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks [64.67735676127208]
  Text-to-image diffusion models have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. We introduce customized solutions by fully exploiting the aforementioned free attention masks.
  arXiv  Detail & Related papers  (2023-08-13T10:07:46Z)
• Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images [6.3351090376024155]
  Uni-Removal is a twostage semi-supervised framework for addressing the removal of multiple degradations in real-world images. In the knowledge transfer stage, Uni-Removal leverages a supervised multi-teacher and student architecture. In the domain adaptation stage, unsupervised fine-tuning is performed by incorporating an adversarial discriminator on real-world images.
  arXiv  Detail & Related papers  (2023-07-11T07:18:15Z)
• DiffusionSeg: Adapting Diffusion Towards Unsupervised Object Discovery [20.787180028571694]
  DiffusionSeg is a synthesis-exploitation framework containing two-stage strategies. We synthesize abundant images, and propose a novel training-free AttentionCut to obtain masks in the first stage. In the second exploitation stage, to bridge the structural gap, we use the inversion technique, to map the given image back to diffusion features.
  arXiv  Detail & Related papers  (2023-03-17T07:47:55Z)
• FD-GAN: Generative Adversarial Networks with Fusion-discriminator for Single Image Dehazing [48.65974971543703]
  We propose a fully end-to-end Generative Adversarial Networks with Fusion-discriminator (FD-GAN) for image dehazing. Our model can generator more natural and realistic dehazed images with less color distortion and fewer artifacts. Experiments have shown that our method reaches state-of-the-art performance on both public synthetic datasets and real-world images.
  arXiv  Detail & Related papers  (2020-01-20T04:36:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.