WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency
<|reference_start|>WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency: Recent advancements in single image super-resolution have been predominantly driven by token mixers and transformer architectures. WaveMixSR utilized the WaveMix architecture, employing a two-dimensional discrete wavelet transform for spatial token mixing, achieving superior performance in super-resolution tasks with remarkable resource efficiency. In this work, we present an enhanced version of the WaveMixSR architecture by (1) replacing the traditional transpose convolution layer with a pixel shuffle operation and (2) implementing a multistage design for higher resolution tasks ($4\times$). Our experiments demonstrate that our enhanced model -- WaveMixSR-V2 -- outperforms other architectures in multiple super-resolution tasks, achieving state-of-the-art for the BSD100 dataset, while also consuming fewer resources, exhibits higher parameter efficiency, lower latency and higher throughput. Our code is available at https://github.com/pranavphoenix/WaveMixSR.<|reference_end|>
@article{jeevan2024wavemixsr-v2:,
    title={WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency},
    author={Pranav Jeevan, Neeraj Nixon, Amit Sethi},
    journal={arXiv preprint arXiv:2409.10582},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10582},
    primaryClass={eess.IV cs.AI cs.CV cs.LG}
}