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2023 | OriginalPaper | Buchkapitel

DWA: Differential Wavelet Amplifier for Image Super-Resolution

verfasst von : Brian B. Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio, Andreas Dengel

Erschienen in: Artificial Neural Networks and Machine Learning – ICANN 2023

Verlag: Springer Nature Switzerland

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Abstract

This work introduces Differential Wavelet Amplifier (DWA), a drop-in module for wavelet-based image Super-Resolution (SR). DWA invigorates an approach recently receiving less attention, namely Discrete Wavelet Transformation (DWT). DWT enables an efficient image representation for SR and reduces the spatial area of its input by a factor of 4, the overall model size, and computation cost, framing it as an attractive approach for sustainable ML. Our proposed DWA model improves wavelet-based SR models by leveraging the difference between two convolutional filters to refine relevant feature extraction in the wavelet domain, emphasizing local contrasts and suppressing common noise in the input signals. We show its effectiveness by integrating it into existing SR models, e.g., DWSR and MWCNN, and demonstrate a clear improvement in classical SR tasks. Moreover, DWA enables a direct application of DWSR and MWCNN to input image space, reducing the DWT representation channel-wise since it omits traditional DWT.

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Titel: DWA: Differential Wavelet Amplifier for Image Super-Resolution
verfasst von: Brian B. Moser
Stanislav Frolov
Federico Raue
Sebastian Palacio
Andreas Dengel
Verlag: Springer Nature Switzerland
Buch: Artificial Neural Networks and Machine Learning – ICANN 2023
Print ISBN: 978-3-031-44209-4

Electronic ISBN: 978-3-031-44210-0

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-44210-0_19

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