  Existing super-resolution (SR) models primarily focus on restoring local
texture details, often neglecting the global semantic information within the
scene. This oversight can lead to the omission of crucial semantic details or
the introduction of inaccurate textures during the recovery process. In our
work, we introduce the Cognitive Super-Resolution (CoSeR) framework, empowering
SR models with the capacity to comprehend low-resolution images. We achieve
this by marrying image appearance and language understanding to generate a
cognitive embedding, which not only activates prior information from large
text-to-image diffusion models but also facilitates the generation of
high-quality reference images to optimize the SR process. To further improve
image fidelity, we propose a novel condition injection scheme called
"All-in-Attention", consolidating all conditional information into a single
module. Consequently, our method successfully restores semantically correct and
photorealistic details, demonstrating state-of-the-art performance across
multiple benchmarks. Code: https://github.com/VINHYU/CoSeR
