Tokenize Image Patches: Global Context Fusion for Effective Haze Removal
  in Large Images
Authors: name: Jiuchen Chen, name: Xinyu Yan, name: Qizhi Xu, name: Kaiqi Li

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.