  Single-image super-resolution (SISR) typically focuses on restoring various
degraded low-resolution (LR) images to a single high-resolution (HR) image.
However, during SISR tasks, it is often challenging for models to
simultaneously maintain high quality and rapid sampling while preserving
diversity in details and texture features. This challenge can lead to issues
such as model collapse, lack of rich details and texture features in the
reconstructed HR images, and excessive time consumption for model sampling. To
address these problems, this paper proposes a Latent Feature-oriented Diffusion
Probability Model (LDDPM). First, we designed a conditional encoder capable of
effectively encoding LR images, reducing the solution space for model image
reconstruction and thereby improving the quality of the reconstructed images.
We then employed a normalized flow and multimodal adversarial training,
learning from complex multimodal distributions, to model the denoising
distribution. Doing so boosts the generative modeling capabilities within a
minimal number of sampling steps. Experimental comparisons of our proposed
model with existing SISR methods on mainstream datasets demonstrate that our
model reconstructs more realistic HR images and achieves better performance on
multiple evaluation metrics, providing a fresh perspective for tackling SISR
tasks.
