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 this https URL

通过使用扩展的预训练文本图像模型进行扩散先验处理，本研究提出了基于频率补偿模块和样本空间多专家混合（SS-MoE）的潜在空间方法，以提高图像超分辨率的性能。