The recent use of diffusion prior, enhanced by pre-trained text-image models, has markedly elevated the performance of image super-resolution (SR). To alleviate the huge computational cost required by pixel-based diffusion SR, latent-based methods utilize a feature encoder to transform the image and then implement the SR image generation in a compact latent space. Nevertheless, there are two major issues that limit the performance of latent-based diffusion. First, the compression of latent space usually causes reconstruction distortion. Second, huge computational cost constrains the parameter scale of the diffusion model. To counteract these issues, we first propose a frequency compensation module that enhances the frequency components from latent space to pixel space. The reconstruction distortion (especially for high-frequency information) can be significantly decreased. Then, we propose to use Sample-Space Mixture of Experts (SS-MoE) to achieve more powerful latent-based SR, which steadily improves the capacity of the model without a significant increase in inference costs. These carefully crafted designs contribute to performance improvements in largely explored 4x blind super-resolution benchmarks and extend to large magnification factors, i.e., 8x image SR benchmarks. The code is available at https://github.com/amandaluof/moe_sr.

翻译：近期，借助预训练图文模型增强的扩散先验，显著提升了图像超分辨率性能。为缓解基于像素的扩散超分辨率所需的高昂计算成本，基于潜在的方法采用特征编码器对图像进行变换，并在紧凑的潜在空间中实现超分辨率图像生成。然而，有两项主要问题限制了基于潜在扩散的性能：首先，潜在空间的压缩通常会导致重建失真；其次，巨大的计算成本制约了扩散模型的参数规模。为解决这些问题，我们首先提出一种频率补偿模块，用于增强从潜在空间到像素空间的频率分量，从而显著降低重建失真（尤其针对高频信息）。随后，我们提出采用采样空间专家混合（SS-MoE）实现更强大的基于潜在的超分辨率，该方法在不显著增加推理成本的情况下稳定提升模型容量。这些精心设计使得模型在广泛探索的4倍盲超分辨率基准测试中取得性能提升，并拓展至大放大倍数场景（即8倍图像超分辨率基准测试）。代码已开源：https://github.com/amandaluof/moe_sr。