WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution

• URL: http://arxiv.org/abs/2307.00430v1
• Date: Sat, 1 Jul 2023 21:25:03 GMT
• Title: WaveMixSR: A Resource-efficient Neural Network for Image Super-resolution
• Authors: Pranav Jeevan, Akella Srinidhi, Pasunuri Prathiba, Amit Sethi
• Abstract summary: We propose a new neural network -- WaveMixSR -- for image super-resolution based on WaveMix architecture. WaveMixSR achieves competitive performance in all datasets and reaches state-of-the-art performance in the BSD100 dataset on multiple super-resolution tasks.
• Score: 2.0477182014909205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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