Asset Details
Do you wish to reserve the book?
Exploring high-quality image deraining Transformer via effective large kernel attention
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Exploring high-quality image deraining Transformer via effective large kernel attention
Do you wish to request the book?
Exploring high-quality image deraining Transformer via effective large kernel attention
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Exploring high-quality image deraining Transformer via effective large kernel attention
2025
Overview
In recent years, Transformer has demonstrated significant performance in single image deraining tasks. However, the standard self-attention in the Transformer makes it difficult to model local features of images effectively. To alleviate the above problem, this paper proposes a high-quality deraining Transformer with
e
ffective
l
arge
k
ernel
a
ttention, named as ELKAformer. The network employs the Transformer-Style Effective Large Kernel Conv-Block (ELKB), which contains 3 key designs: Large Kernel Attention Block (LKAB), Dynamical Enhancement Feed-forward Network (DEFN), and Edge Squeeze Recovery Block (ESRB) to guide the extraction of rich features. To be specific, LKAB introduces convolutional modulation to substitute vanilla self-attention and achieve better local representations. The designed DEFN refines the most valuable attention values in LKAB, allowing the overall design to better preserve pixel-wise information. Additionally, we develop ESRB to obtain long-range dependencies of different positional information. Massive experimental results demonstrate that this method achieves favorable effects while effectively saving computational costs. Our code is available at
github
Publisher
Springer Berlin Heidelberg,Springer Nature B.V
