图像块标记化：全局上下文融合实现大图像高效去雾

Tokenize Image Patches: Global Context Fusion for Effective Haze Removal in Large Images

April 13, 2025

作者: Jiuchen Chen, Xinyu Yan, Qizhi Xu, Kaiqi Li

cs.AI

摘要

全局上下文信息与局部细节特征对于去雾任务至关重要。深度学习模型在小尺寸、低分辨率图像上表现优异，但在处理大尺寸、高分辨率图像时，由于GPU内存限制，常面临困难。作为折中方案，这些模型往往采用图像切片或下采样处理。前者削弱了全局信息，后者则丢失了高频细节。为解决这些挑战，我们提出了DehazeXL，一种有效平衡全局上下文与局部特征提取的去雾方法，使得在大规模图像上实现端到端建模成为可能，且适用于主流GPU硬件。此外，为评估全局上下文利用效率对去雾性能的影响，我们设计了一种针对去雾任务特性的视觉归因方法。最后，鉴于大尺寸图像去雾领域缺乏基准数据集，我们开发了一个超高分辨率去雾数据集（8KDehaze），以支持模型的训练与测试。该数据集包含10000对清晰与雾霾遥感图像，每幅图像尺寸为8192×8192像素。大量实验表明，DehazeXL仅需21GB内存即可推理高达10240×10240像素的图像，在所有评估方法中达到了最先进的性能。源代码与实验数据集已发布于https://github.com/CastleChen339/DehazeXL。

English

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.