Removing adverse weather conditions like rain, fog, and snow from images is
an important problem in many applications. Most methods proposed in the
literature have been designed to deal with just removing one type of
degradation. Recently, a CNN-based method using neural architecture search
(All-in-One) was proposed to remove all the weather conditions at once.
However, it has a large number of parameters as it uses multiple encoders to
cater to each weather removal task and still has scope for improvement in its
performance. In this work, we focus on developing an efficient solution for the
all adverse weather removal problem. To this end, we propose TransWeather, a
transformer-based end-to-end model with just a single encoder and a decoder
that can restore an image degraded by any weather condition. Specifically, we
utilize a novel transformer encoder using intra-patch transformer blocks to
enhance attention inside the patches to effectively remove smaller weather
degradations. We also introduce a transformer decoder with learnable weather
type embeddings to adjust to the weather degradation at hand. TransWeather
achieves improvements across multiple test datasets over both All-in-One
network as well as methods fine-tuned for specific tasks. TransWeather is also
validated on real world test images and found to be more effective than
previous methods. Implementation code can be accessed at
this https URL .

本研究提出了一种基于 Transformer 的端到端模型 TransWeather，只使用一个编码器和一个解码器，可以有效去除任何天气条件下的图像失真，其在多个测试数据集上均取得了比 All-in-One 方法和 Fine-tuned 方法更好的效果，并在现实世界的测试图像上进行了验证。