  This paper presents a novel Diffusion-Wavelet (DiWa) approach for
Single-Image Super-Resolution (SISR). It leverages the strengths of Denoising
Diffusion Probabilistic Models (DDPMs) and Discrete Wavelet Transformation
(DWT). By enabling DDPMs to operate in the DWT domain, our DDPM models
effectively hallucinate high-frequency information for super-resolved images on
the wavelet spectrum, resulting in high-quality and detailed reconstructions in
image space. Quantitatively, we outperform state-of-the-art diffusion-based
SISR methods, namely SR3 and SRDiff, regarding PSNR, SSIM, and LPIPS on both
face (8x scaling) and general (4x scaling) SR benchmarks. Meanwhile, using DWT
enabled us to use fewer parameters than the compared models: 92M parameters
instead of 550M compared to SR3 and 9.3M instead of 12M compared to SRDiff.
Additionally, our method outperforms other state-of-the-art generative methods
on classical general SR datasets while saving inference time. Finally, our work
highlights its potential for various applications.
