  Deep neural networks have greatly promoted the performance of single image
super-resolution (SISR). Conventional methods still resort to restoring the
single high-resolution (HR) solution only based on the input of image modality.
However, the image-level information is insufficient to predict adequate
details and photo-realistic visual quality facing large upscaling factors (x8,
x16). In this paper, we propose a new perspective that regards the SISR as a
semantic image detail enhancement problem to generate semantically reasonable
HR image that are faithful to the ground truth. To enhance the semantic
accuracy and the visual quality of the reconstructed image, we explore the
multi-modal fusion learning in SISR by proposing a Text-Guided Super-Resolution
(TGSR) framework, which can effectively utilize the information from the text
and image modalities. Different from existing methods, the proposed TGSR could
generate HR image details that match the text descriptions through a
coarse-to-fine process. Extensive experiments and ablation studies demonstrate
the effect of the TGSR, which exploits the text reference to recover realistic
images.
