  Image super-resolution research recently been dominated by transformer models
which need higher computational resources than CNNs due to the quadratic
complexity of self-attention. We propose a new neural network -- WaveMixSR --
for image super-resolution based on WaveMix architecture which uses a
2D-discrete wavelet transform for spatial token-mixing. Unlike
transformer-based models, WaveMixSR does not unroll the image as a sequence of
pixels/patches. It uses the inductive bias of convolutions along with the
lossless token-mixing property of wavelet transform to achieve higher
performance while requiring fewer resources and training data. We compare the
performance of our network with other state-of-the-art methods for image
super-resolution. Our experiments show that WaveMixSR achieves competitive
performance in all datasets and reaches state-of-the-art performance in the
BSD100 dataset on multiple super-resolution tasks. Our model is able to achieve
this performance using less training data and computational resources while
maintaining high parameter efficiency compared to current state-of-the-art
models.
