  Convolutional neural networks (CNN) have demonstrated outstanding Compressed
Sensing (CS) performance compared to traditional, hand-crafted methods.
However, they are broadly limited in terms of generalisability, inductive bias
and difficulty to model long distance relationships. Transformer neural
networks (TNN) overcome such issues by implementing an attention mechanism
designed to capture dependencies between inputs. However, high-resolution tasks
typically require vision Transformers (ViT) to decompose an image into
patch-based tokens, limiting inputs to inherently local contexts. We propose a
novel image decomposition that naturally embeds images into low-resolution
inputs. These Kaleidoscope tokens (KD) provide a mechanism for global
attention, at the same computational cost as a patch-based approach. To
showcase this development, we replace CNN components in a well-known CS-MRI
neural network with TNN blocks and demonstrate the improvements afforded by KD.
We also propose an ensemble of image tokens, which enhance overall image
quality and reduces model size. Supplementary material is available:
https://github.com/uqmarlonbran/TCS.git
