Encoder-minimal and Decoder-minimal Framework for Remote Sensing Image
  Dehazing
Yuanbo Wen Tao Gao Ziqi Li Jing Zhang Ting Chen
Haze obscures remote sensing images, hindering valuable information
extraction. To this end, we propose RSHazeNet, an encoder-minimal and
decoder-minimal framework for efficient remote sensing image dehazing.
Specifically, regarding the process of merging features within the same level,
we develop an innovative module called intra-level transposed fusion module
(ITFM). This module employs adaptive transposed self-attention to capture
comprehensive context-aware information, facilitating the robust context-aware
feature fusion. Meanwhile, we present a cross-level multi-view interaction
module (CMIM) to enable effective interactions between features from various
levels, mitigating the loss of information due to the repeated sampling
operations. In addition, we propose a multi-view progressive extraction block
(MPEB) that partitions the features into four distinct components and employs
convolution with varying kernel sizes, groups, and dilation factors to
facilitate view-progressive feature learning. Extensive experiments demonstrate
the superiority of our proposed RSHazeNet. We release the source code and all
pre-trained models at \url{https://github.com/chdwyb/RSHazeNet}.