  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
