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Eliminating the rain degradation in stereo images poses a formidablechallenge, which necessitates the efficient exploitation of mutual informationpresent between the dual views. To this end, we devise MQINet, which employsmulti-dimension queries and interactions for stereo image deraining. Morespecifically, our approach incorporates a context-aware dimension-wise queriedblock (CDQB). This module leverages dimension-wise queries that are independentof the input features and employs global context-aware attention (GCA) tocapture essential features while avoiding the entanglement of redundant orirrelevant information. Meanwhile, we introduce an intra-view physics-awareattention (IPA) based on the inverse physical model of rainy images. IPAextracts shallow features that are sensitive to the physics of raindegradation, facilitating the reduction of rain-related artifacts during theearly learning period. Furthermore, we integrate a cross-view multi-dimensioninteracting attention mechanism (CMIA) to foster comprehensive featureinteraction between the two views across multiple dimensions. Extensiveexperimental evaluations demonstrate the superiority of our model over EPRRNetand StereoIRR, achieving respective improvements of 4.18 dB and 0.45 dB inPSNR. Code and models are available at \url{https://github.com/chdwyb/MQINet}.

Multi-dimension Queried and Interacting Network for Stereo Image Deraining