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：近年来，利用预训练文本-图像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巨大计算成本，基于潜空间的方法采用特征编码器对图像进行变换，并在紧凑的潜空间中实现超分辨率图像生成。然而，当前存在两个主要问题限制了基于潜空间扩散的性能：第一，潜空间的压缩通常导致重建失真；第二，巨大的计算成本限制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，用于增强从潜空间到像素空间的频率分量，从而显著降低重建失真（特别是高频信息）。随后，我们提出采用采样空间专家混合（SS-MoE）来实现更强大的基于潜空间的超分辨率，该方法在推理成本不显著增加的情况下稳定提升了模型容量。这些精心设计在广泛探索的4倍盲超分辨率基准测试中带来了性能提升，并扩展到高倍放大因子（即8倍图像超分辨率基准测试）。代码已开源在https://github.com/amandaluof/moe_sr。