| Summary: | Super-resolution (SR) is a fundamental task in computer vision aimed at enhancing the resolution and quality of low-resolution images. However, a persistent challenge arises from the inherent ambiguity where a single low-resolution image may correspond to mul- tiple high-resolution images. Additional priors are essential to address such problem, especially when the degradation is complex. Recent emergence of large vision-language model such as CLIP provides potential to enhance SR generation by providing extra con- textual information from the image. Hence, in this project, we investigate the efficacy of integrating CLIP priors into image super-resolution. Through a series of experiments, we explore both blind and non-blind SR problems, evaluating the impact of CLIP priors on model performance. Additionally, we analyze the limitations and challenges associated with CLIP integration, particularly in handling low-resolution and incomplete images. Our findings demonstrate that while CLIP priors hold promise in enhancing SR results, careful fine-tuning is required to optimize their utilization for image generation tasks.
|
|---|