Stripformer: Strip Transformer for Fast Image Deblurring
By Fu-Jen Tsai, Yan-Tsung Peng, Yen-Yu Lin, Chung-Chi Tsai, Chia-Wen Lin. European Conference on Computer Vision, 2022
323 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Media Technology
View Paper: https://www.semanticscholar.org/paper/aab4ed4ee6bad74d4c07fa002da476504c62fda9
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In Computer Vision and Pattern Recognition
In Media Technology
Citations in the corpus
- Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring (2022, methodology): “In addition, compared to the Transformer-based methods [21,24,27], our method has the fewest model parameters while the performance is better.”