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Haze obscures remote sensing images, hindering valuable informationextraction. To this end, we propose RSHazeNet, an encoder-minimal anddecoder-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 capturecomprehensive context-aware information, facilitating the robust context-awarefeature fusion. Meanwhile, we present a cross-level multi-view interactionmodule (CMIM) to enable effective interactions between features from variouslevels, mitigating the loss of information due to the repeated samplingoperations. In addition, we propose a multi-view progressive extraction block(MPEB) that partitions the features into four distinct components and employsconvolution with varying kernel sizes, groups, and dilation factors tofacilitate view-progressive feature learning. Extensive experiments demonstratethe superiority of our proposed RSHazeNet. We release the source code and allpre-trained models at \url{https://github.com/chdwyb/RSHazeNet}.

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