  The objective of image super-resolution is to generate clean and
high-resolution images from degraded versions. Recent advancements in diffusion
modeling have led to the emergence of various image super-resolution techniques
that leverage pretrained text-to-image (T2I) models. Nevertheless, due to the
prevalent severe degradation in low-resolution images and the inherent
characteristics of diffusion models, achieving high-fidelity image restoration
remains challenging. Existing methods often exhibit issues including semantic
loss, artifacts, and the introduction of spurious content not present in the
original image. To tackle this challenge, we propose Cascaded diffusion for
Super-Resolution, CasSR , a novel method designed to produce highly detailed
and realistic images. In particular, we develop a cascaded controllable
diffusion model that aims to optimize the extraction of information from
low-resolution images. This model generates a preliminary reference image to
facilitate initial information extraction and degradation mitigation.
Furthermore, we propose a multi-attention mechanism to enhance the T2I model's
capability in maximizing the restoration of the original image content. Through
a comprehensive blend of qualitative and quantitative analyses, we substantiate
the efficacy and superiority of our approach.
