Papers › WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution
WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution
Pranav Jeevan, Akella Srinidhi, Pasunuri Prathiba, Amit Sethi
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.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR | PSNR | 33.08 | #2 of 30 | Archive leaderboard | report |
| Image Super-Resolution | BSD100 - 2x upscaling | WaveMixSR | SSIM | 0.9322 | #2 of 30 | Archive leaderboard | report |
| Image Super-Resolution | BSD100 - 4x upscaling | WaveMixSR | SSIM | 0.7605 | #69 of 71 | Archive leaderboard | report |
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