WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency

• Published in: arXiv.org
• Main Authors: Jeevan, Pranav, Nixon, Neeraj, Sethi, Amit
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 16.09.2024
• Subjects: Discrete Wavelet Transform; Efficiency; Image enhancement; Image resolution; Mixers

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.