WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency

• URL: http://arxiv.org/abs/2409.10582v3
• Date: Wed, 30 Oct 2024 15:16:43 GMT
• Title: WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency
• Authors: Pranav Jeevan, Neeraj Nixon, Amit Sethi,
• Abstract summary: We present an enhanced version of the WaveMixSR architecture by replacing the traditional convolution layer with a pixel shuffle operation. Our experiments demonstrate that our enhanced model -- WaveMixSR-V2 -- outperforms other architectures in multiple super-resolution tasks.
• Score: 4.093503153499691
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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