Tokenize Image Patches: Global Context Fusion for Effective Haze Removal
  in Large Images
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.
6800ef5509eaa9d1d87a6eaf
Jiuchen Chen
claimed_verified
2025-04-22T10:13:24.993Z
66d12725c75e3c02420c80b5
/avatars/cc0e5629e44f5d83362fc678d951f7bd.svg
Jiuchen Chen
user
CastleChen339
6800ef5509eaa9d1d87a6eb0
Xinyu Yan
admin_assigned
2025-04-19T15:17:09.181Z
6672c01fa6eb488f049ecb80
https://cdn-avatars.huggingface.co/v1/production/uploads/6672c01fa6eb488f049ecb80/f6SetkETOWgyXy1KmBPhK.jpeg
Xinyu Yan
user
fengyanzi
6800ef5509eaa9d1d87a6eb1
Qizhi Xu
6800ef5509eaa9d1d87a6eb2
Kaiqi Li
Tokenize Image Patches: Global Context Fusion for Effective Haze Removal
  in Large Images
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.
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.
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