Wavelet-Space Super-Resolution for Real-Time Rendering

Abstract: We investigate the use of wavelet-space feature decomposition in neural super-resolution for rendering pipelines. Building on the DFASR framework, we introduce a wavelet-domain representation that separates low- and high-frequency details before reconstruction, enabling the network to better preserve fine textures while maintaining structural consistency. Unlike RGB-space regression, our approach leverages the stationary wavelet transform (SWT) to avoid spatial down-sampling, ensuring alignment across subbands and preserving shift invariance. The model predicts wavelet coefficients conditioned on spatial G-buffers and temporally warped history frames, which are then recombined through inverse wavelet synthesis. We conduct a comprehensive ablation study across wavelet families, transform types, and architectural variants, showing that incorporating SWT improves PSNR by up to 1.5 dB and reduces LPIPS by 17% on average, at a computational overhead of roughly +24 ms compared to out DFASR baseline. While absolute runtimes on our RTX 3050 mobile GPU are higher ( 141ms) than the original DFASR report on RTX 4090( 11ms), the relative overhead remains modest, suggesting that on higher-end GPUs our method would also remain real-time capable. Taken together, our results suggest that wavelet-domain representations are a principled and effective way to enhance perceptual quality in neural upscaling for graphics applications.