DedustNet: A Frequency-dominated Swin Transformer-based Wavelet Network
  for Agricultural Dust Removal
Shengli Zhang Zhiyong Tao Sen Lin
While dust significantly affects the environmental perception of automated
agricultural machines, the existing deep learning-based methods for dust
removal require further research and improvement in this area to improve the
performance and reliability of automated agricultural machines in agriculture.
We propose an end-to-end trainable learning network (DedustNet) to solve the
real-world agricultural dust removal task. To our knowledge, DedustNet is the
first time Swin Transformer-based units have been used in wavelet networks for
agricultural image dusting. Specifically, we present the frequency-dominated
block (DWTFormer block and IDWTFormer block) by adding a spatial features
aggregation scheme (SFAS) to the Swin Transformer and combining it with the
wavelet transform, the DWTFormer block and IDWTFormer block, alleviating the
limitation of the global receptive field of Swin Transformer when dealing with
complex dusty backgrounds. Furthermore, We propose a cross-level information
fusion module to fuse different levels of features and effectively capture
global and long-range feature relationships. In addition, we present a dilated
convolution module to capture contextual information guided by wavelet
transform at multiple scales, which combines the advantages of wavelet
transform and dilated convolution. Our algorithm leverages deep learning
techniques to effectively remove dust from images while preserving the original
structural and textural features. Compared to existing state-of-the-art
methods, DedustNet achieves superior performance and more reliable results in
agricultural image dedusting, providing strong support for the application of
agricultural machinery in dusty environments. Additionally, the impressive
performance on real-world hazy datasets and application tests highlights
DedustNet superior generalization ability and computer vision-related
application performance.