  Images taken in dynamic scenes may contain unwanted motion blur, which
significantly degrades visual quality. Such blur causes short- and long-range
region-specific smoothing artifacts that are often directional and non-uniform,
which is difficult to be removed. Inspired by the current success of
transformers on computer vision and image processing tasks, we develop,
Stripformer, a transformer-based architecture that constructs intra- and
inter-strip tokens to reweight image features in the horizontal and vertical
directions to catch blurred patterns with different orientations. It stacks
interlaced intra-strip and inter-strip attention layers to reveal blur
magnitudes. In addition to detecting region-specific blurred patterns of
various orientations and magnitudes, Stripformer is also a token-efficient and
parameter-efficient transformer model, demanding much less memory usage and
computation cost than the vanilla transformer but works better without relying
on tremendous training data. Experimental results show that Stripformer
performs favorably against state-of-the-art models in dynamic scene deblurring.
