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For the problem of image degradation caused by raindrops attached to the lens or glass surface, this paper proposed a multi-stage progressive image raindrop removal method which divides the raindrop removal process into multiple stages to easily implement. First, the multi-scale fused encoder-decoder network at each stage designed to learn raindrop features. The constructed multi-scale dilated convolutions with gated recurrent units refined the internally transferred spatial features. In addition, the no dimensionality reduction channel attention module extracted channel information under specific spatial features. Finally, this paper adopted cross-stage feature fusion mechanism to strengthen the information exchange between the parts of each stage. Adding horizontal connections between the encoder-decoder networks in each stage to realize the horizontal transfer of feature information. And adding a supervised attention module to enhance the information transmission between different stages. The raindrop removal is progressively achieved. Experiments show that the proposed method can effectively remove raindrops. [ABSTRACT FROM AUTHOR]