title: Tokenize Image Patches: Global Context Fusion for Effective Haze Removal
    in Large Images
summary: Global contextual information and local detail features are essential for
  haze removal tasks. Deep learning models perform well on small, low-resolution
  images, but they encounter difficulties with large, high-resolution ones due to
  GPU memory limitations. As a compromise, they often resort to image slicing or
  downsampling. The former diminishes global information, while the latter
  discards high-frequency details. To address these challenges, we propose
  DehazeXL, a haze removal method that effectively balances global context and
  local feature extraction, enabling end-to-end modeling of large images on
  mainstream GPU hardware. Additionally, to evaluate the efficiency of global
  context utilization in haze removal performance, we design a visual attribution
  method tailored to the characteristics of haze removal tasks. Finally,
  recognizing the lack of benchmark datasets for haze removal in large images, we
  have developed an ultra-high-resolution haze removal dataset (8KDehaze) to
  support model training and testing. It includes 10000 pairs of clear and hazy
  remote sensing images, each sized at 8192 times 8192 pixels. Extensive
  experiments demonstrate that DehazeXL can infer images up to 10240 times
  10240 pixels with only 21 GB of memory, achieving state-of-the-art results
  among all evaluated methods. The source code and experimental dataset are
  available at https://github.com/CastleChen339/DehazeXL.
paper_authors._id: 6800ef5509eaa9d1d87a6eaf
paper_authors.name: Jiuchen Chen
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-22T10:13:24.993Z
paper_authors._id: 66d12725c75e3c02420c80b5
paper_authors.avatarUrl: /avatars/cc0e5629e44f5d83362fc678d951f7bd.svg
paper_authors.fullname: Jiuchen Chen
paper_authors.type: user
paper_authors.user: CastleChen339
paper_authors._id: 6800ef5509eaa9d1d87a6eb0
paper_authors.name: Xinyu Yan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-04-19T15:17:09.181Z
paper_authors._id: 6672c01fa6eb488f049ecb80
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6672c01fa6eb488f049ecb80/f6SetkETOWgyXy1KmBPhK.jpeg
paper_authors.fullname: Xinyu Yan
paper_authors.type: user
paper_authors.user: fengyanzi
paper_authors._id: 6800ef5509eaa9d1d87a6eb1
paper_authors.name: Qizhi Xu
paper_authors._id: 6800ef5509eaa9d1d87a6eb2
paper_authors.name: Kaiqi Li
paper_title: Tokenize Image Patches: Global Context Fusion for Effective Haze Removal
    in Large Images
paper_summary: Global contextual information and local detail features are essential for
  haze removal tasks. Deep learning models perform well on small, low-resolution
  images, but they encounter difficulties with large, high-resolution ones due to
  GPU memory limitations. As a compromise, they often resort to image slicing or
  downsampling. The former diminishes global information, while the latter
  discards high-frequency details. To address these challenges, we propose
  DehazeXL, a haze removal method that effectively balances global context and
  local feature extraction, enabling end-to-end modeling of large images on
  mainstream GPU hardware. Additionally, to evaluate the efficiency of global
  context utilization in haze removal performance, we design a visual attribution
  method tailored to the characteristics of haze removal tasks. Finally,
  recognizing the lack of benchmark datasets for haze removal in large images, we
  have developed an ultra-high-resolution haze removal dataset (8KDehaze) to
  support model training and testing. It includes 10000 pairs of clear and hazy
  remote sensing images, each sized at 8192 times 8192 pixels. Extensive
  experiments demonstrate that DehazeXL can infer images up to 10240 times
  10240 pixels with only 21 GB of memory, achieving state-of-the-art results
  among all evaluated methods. The source code and experimental dataset are
  available at https://github.com/CastleChen339/DehazeXL.
paper_ai_summary: DehazeXL is a haze removal method that balances global context and local features, enabling efficient processing of large high-resolution images using mainstream GPU hardware.
paper_ai_keywords: global contextual information local detail features deep learning models image slicing downsampling end-to-end modeling visual attribution method benchmark datasets ultra-high-resolution haze removal dataset 8KDehaze inference