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:本文提出了一种新颖的扩散小波(DiWa)方法,用于单幅图像超分辨率(SISR)。它利用了去噪扩散概率模型(DDPMs)和离散小波变换(DWT)的强项。通过使DDPM在DWT域中运行,我们的DDPM模型有效地在小波频谱上为超分辨图像产生高频信息,从而在图像空间中产生高质量和详细的重建。量化地,在人脸(8倍缩放)和普通(4倍缩放)SR基准上,我们超越了最先进的基于扩散的SISR方法,即SR3和SRDiff,这表现为PSNR、 SSIM和LPIPS。同时,使用DWT让我们可以使用比对比模型少的参数:92M参数而不是对比SR3的550M和9.3M而不是对比SRDiff的12M。此外,我们的方法在古典的普通SR数据集上优于其他最先进的生成方法,并节省了推断时间。最后,我们的工作突出了它在各种应用中的潜力。