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High dynamic range (HDR) imaging aims to retrieve information from multiplelow-dynamic range inputs to generate realistic output. The essence is toleverage the contextual information, including both dynamic and staticsemantics, for better image generation. Existing methods often focus on thespatial misalignment across input frames caused by the foreground and/or cameramotion. However, there is no research on jointly leveraging the dynamic andstatic context in a simultaneous manner. To delve into this problem, we proposea novel alignment-free network with a Semantics Consistent Transformer (SCTNet)with both spatial and channel attention modules in the network. The spatialattention aims to deal with the intra-image correlation to model the dynamicmotion, while the channel attention enables the inter-image intertwining toenhance the semantic consistency across frames. Aside from this, we introduce anovel realistic HDR dataset with more variations in foreground objects,environmental factors, and larger motions. Extensive comparisons on bothconventional datasets and ours validate the effectiveness of our method,achieving the best trade-off on the performance and the computational cost.

Alignment-free HDR Deghosting with Semantics Consistent Transformer