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.

翻译：本文提出了一种用于单图像超分辨率（SISR）的新型扩散-小波（DiWa）方法。该方法结合了去噪扩散概率模型（DDPMs）和离散小波变换（DWT）的优势。通过使DDPMs在小波域中运行，我们的DDPM模型能够有效在小波频谱上为超分辨率图像生成高频信息，从而在图像空间中实现高质量且细节丰富的重建结果。在定量评估中，我们在人脸（8倍缩放）和通用（4倍缩放）超分辨率基准测试上，分别在PSNR、SSIM和LPIPS指标上超越了基于扩散的先进SISR方法SR3和SRDiff。同时，利用DWT使我们能够使用更少的参数：与SR3相比，参数从550M降至92M；与SRDiff相比，参数从12M降至9.3M。此外，我们的方法在经典通用超分辨率数据集上优于其他先进的生成式方法，同时节省了推理时间。最后，本文凸显了该方法在多种应用中的潜力。