  Fusing a sequence of perfectly aligned images captured at various exposures,
has shown great potential to approach High Dynamic Range (HDR) imaging by
sensors with limited dynamic range. However, in the presence of large motion of
scene objects or the camera, mis-alignment is almost inevitable and leads to
the notorious ``ghost'' artifacts. Besides, factors such as the 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 novel
multi-exposure fusion model based on Swin Transformer. Particularly, we design
feature selection gates, which are integrated with the feature extraction
layers to detect outliers and block them from HDR image synthesis. To
reconstruct the missing local details by well-aligned and properly-exposed
regions, 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 the accuracy on par with current top
performing multi-exposure HDR imaging models, while gaining higher efficiency.
