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：近来，在预训练文本-图像模型增强下，利用扩散先验的方法显著提升了图像超分辨率（SR）的性能。为缓解基于像素的扩散SR所需的高昂计算成本，基于潜在空间的方法利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SR图像生成。然而，有两个主要问题限制了基于潜在空间的扩散性能：第一，潜在空间的压缩通常导致重建失真；第二，巨大的计算成本制约了扩散模型的参数规模。为应对这些问题，我们首先提出一种频率补偿模块，该模块增强了从潜在空间到像素空间的频率分量，从而显著减少了重建失真（尤其是高频信息）。然后，我们提出使用采样空间专家混合（SS-MoE）来实现更强大的基于潜在空间的SR，该方法在不显著增加推理成本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，并扩展到大的放大因子，即8倍图像SR基准测试。代码可在https://github.com/amandaluof/moe_sr获取。