Encoder-minimal and Decoder-minimal Framework for Remote Sensing Image Dehazing

• URL: http://arxiv.org/abs/2312.07849v1
• Date: Wed, 13 Dec 2023 02:35:02 GMT
• Title: Encoder-minimal and Decoder-minimal Framework for Remote Sensing Image Dehazing
• Authors: Yuanbo Wen, Tao Gao, Ziqi Li, Jing Zhang, Ting Chen
• Abstract summary: RSHazeNet is an encoder-minimal and decoder-minimal framework for efficient remote sensing image dehazing. We develop an innovative module called intra-level transposed fusion module (ITFM)
• Score: 13.759978932686519
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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}.

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