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.

翻译：本文提出差分小波放大器（DWA），一种即插即用的基于小波的图像超分辨率（SR）模块。DWA重新激活了最近较少受到关注的离散小波变换（DWT）方法。DWT为超分辨率提供了高效的图像表示，并将输入空间面积缩小至原来的四分之一，同时降低了整体模型尺寸和计算成本，使其成为可持续机器学习中具有吸引力的方法。本文提出的DWA模型通过利用两个卷积滤波器之间的差异，在小波域中优化相关特征提取，突出局部对比度并抑制输入信号中的常见噪声，从而改进了基于小波的超分辨率模型。通过将其集成到现有超分辨率模型（例如DWSR和MWCNN）中，我们展示了其在经典超分辨率任务中的显著改进效果。此外，DWA使得DWSR和MWCNN能够直接应用于输入图像空间，由于省略了传统DWT，从而减少了DWT表示的通道数量。