CrossDehaze: Scaling Up Image Dehazing with Cross-Data Vision Alignment and Augmentation

• URL: http://arxiv.org/abs/2407.14823v1
• Date: Sat, 20 Jul 2024 10:00:20 GMT
• Title: CrossDehaze: Scaling Up Image Dehazing with Cross-Data Vision Alignment and Augmentation
• Authors: Yukai Shi, Zhipeng Weng, Yupei Lin, Cidan Shi, Xiaojun Yang, Liang Lin,
• Abstract summary: Methods based on priors and deep learning have been proposed to address the task of image dehazing. We propose a novel method of internal and external data augmentation to improve the existing dehazing methodology. Our approach significantly outperforms other advanced methods in dehazing and produces dehazed images that are closest to real haze-free images.
• Score: 47.425906124301775
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recent years, as computer vision tasks have increasingly relied on high-quality image inputs, the task of image dehazing has received significant attention. Previously, many methods based on priors and deep learning have been proposed to address the task of image dehazing. Ignoring the domain gap between different data, former de-hazing methods usually adopt multiple datasets for explicit training, which often makes the methods themselves be violated. To address this problem, we propose a novel method of internal and external data augmentation to improve the existing dehazing methodology. By using cross-data external augmentor. The dataset inherits samples from different domains that are firmly aligned, making the model learn more robust and generalizable features. By using the internal data augmentation method, the model can fully exploit local information within the images, thereby obtaining more image details. To demonstrate the effectiveness of our proposed method, we conduct training on both the Natural Image Dataset (NID) and the Remote Sensing Image Dataset (RSID). Experimental results show that our method clearly resolves the domain gap in different dehazing datasets and presents a new pipeline for joint training in the dehazing task. Our approach significantly outperforms other advanced methods in dehazing and produces dehazed images that are closest to real haze-free images. The code will be available at: https://github.com/wengzp1/ScaleUpDehazing

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