Real-world Image Dehazing with Coherence-based Label Generator and Cooperative Unfolding Network

• URL: http://arxiv.org/abs/2406.07966v3
• Date: Sat, 26 Oct 2024 06:44:52 GMT
• Title: Real-world Image Dehazing with Coherence-based Label Generator and Cooperative Unfolding Network
• Authors: Chengyu Fang, Chunming He, Fengyang Xiao, Yulun Zhang, Longxiang Tang, Yuelin Zhang, Kai Li, Xiu Li,
• Abstract summary: Real-world Image Dehazing aims to alleviate haze-induced degradation in real-world settings. We introduce a cooperative unfolding network that jointly models atmospheric scattering and image scenes. We also propose the first RID-oriented iterative mean-teacher framework, termed the Coherence-based Label Generator.
• Score: 50.31598963315055
• License:
• Abstract: Real-world Image Dehazing (RID) aims to alleviate haze-induced degradation in real-world settings. This task remains challenging due to the complexities in accurately modeling real haze distributions and the scarcity of paired real-world data. To address these challenges, we first introduce a cooperative unfolding network that jointly models atmospheric scattering and image scenes, effectively integrating physical knowledge into deep networks to restore haze-contaminated details. Additionally, we propose the first RID-oriented iterative mean-teacher framework, termed the Coherence-based Label Generator, to generate high-quality pseudo labels for network training. Specifically, we provide an optimal label pool to store the best pseudo-labels during network training, leveraging both global and local coherence to select high-quality candidates and assign weights to prioritize haze-free regions. We verify the effectiveness of our method, with experiments demonstrating that it achieves state-of-the-art performance on RID tasks. Code will be available at \url{https://github.com/cnyvfang/CORUN-Colabator}.

Related papers

• Contrasting Deepfakes Diffusion via Contrastive Learning and Global-Local Similarities [88.398085358514]
  Contrastive Deepfake Embeddings (CoDE) is a novel embedding space specifically designed for deepfake detection. CoDE is trained via contrastive learning by additionally enforcing global-local similarities.
  arXiv  Detail & Related papers  (2024-07-29T18:00:10Z)
• RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors [14.432465539590481]
  Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. We present a new paradigm for real image dehazing from the perspectives of synthesizing more realistic hazy data.
  arXiv  Detail & Related papers  (2023-04-08T12:12:24Z)
• Imposing Consistency for Optical Flow Estimation [73.53204596544472]
  Imposing consistency through proxy tasks has been shown to enhance data-driven learning. This paper introduces novel and effective consistency strategies for optical flow estimation.
  arXiv  Detail & Related papers  (2022-04-14T22:58:30Z)
• NetRCA: An Effective Network Fault Cause Localization Algorithm [22.88986905436378]
  Localizing root cause of network faults is crucial to network operation and maintenance. We propose a novel algorithm named NetRCA to deal with this problem. Experiments and analysis are conducted on the real-world dataset from ICASSP 2022 AIOps Challenge.
  arXiv  Detail & Related papers  (2022-02-23T02:03:35Z)
• From Synthetic to Real: Image Dehazing Collaborating with Unlabeled Real Data [58.50411487497146]
  We propose a novel image dehazing framework collaborating with unlabeled real data. First, we develop a disentangled image dehazing network (DID-Net), which disentangles the feature representations into three component maps. Then a disentangled-consistency mean-teacher network (DMT-Net) is employed to collaborate unlabeled real data for boosting single image dehazing.
  arXiv  Detail & Related papers  (2021-08-06T04:00:28Z)
• Dehaze-GLCGAN: Unpaired Single Image De-hazing via Adversarial Training [3.5788754401889014]
  We propose a dehazing Global-Local Cycle-consistent Generative Adversarial Network (Dehaze-GLCGAN) for single image de-hazing. Our experiments over three benchmark datasets show that our network outperforms previous work in terms of PSNR and SSIM.
  arXiv  Detail & Related papers  (2020-08-15T02:43:00Z)
• A U-Net Based Discriminator for Generative Adversarial Networks [86.67102929147592]
  We propose an alternative U-Net based discriminator architecture for generative adversarial networks (GANs) The proposed architecture allows to provide detailed per-pixel feedback to the generator while maintaining the global coherence of synthesized images. The novel discriminator improves over the state of the art in terms of the standard distribution and image quality metrics.
  arXiv  Detail & Related papers  (2020-02-28T11:16:54Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.