Motion-robust multi-exposure HDR computational imaging based on gated Swin Transformer

Fusing a sequence of static images captured at various exposures, has shown great potential to approach High Dynamic Range (HDR) imaging. However, in the presence of large motion, mis-alignment is almost inevitable and leads to the ghost artifacts. Besides, factors such as noise in the dark region or color saturation in the over-bright region may also fail to fill local image details to the HDR image. This paper provides a motion-robust multi-exposure fusion model based on Swin Transformer. Particularly, we design feature selection gates, which are integrated with the feature warping layers to detect and block mis-alignments. To reconstruct the missing local details, we exploit the long distance contextual dependency in the exposure-space pyramid by the self-attention mechanism. Extensive numerical and visual evaluation has been conducted on a variety of benchmark datasets. The experiments show that our model achieves superior accuracy and efficacy to existing methods.