DWA: Differential Wavelet Amplifier for Image Super-Resolution
Brian B. Moser Stanislav Frolov Federico Raue Sebastian Palacio Andreas Dengel
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.