RAGSR: Regional Attention Guided Diffusion for Image Super-Resolution

TL;DR

RAGSR introduces a regional attention mechanism that enhances image super-resolution by explicitly incorporating localized textual descriptions, leading to more detailed and contextually consistent high-resolution images.

Contribution

The paper proposes a novel regional attention guided super-resolution method that explicitly encodes localized textual information to improve detail and accuracy in SR results.

Findings

Outperforms existing methods in perceptual quality and detail preservation

Effectively localizes object regions and encodes fine-grained descriptions

Achieves superior results on benchmark datasets

Abstract

The rich textual information of large vision-language models (VLMs) combined with the powerful generative prior of pre-trained text-to-image (T2I) diffusion models has achieved impressive performance in single-image super-resolution (SISR). However, existing methods still face significant challenges in generating clear and accurate regional details, particularly in scenarios involving multiple objects. This challenge primarily stems from a lack of fine-grained regional descriptions and the models' insufficient ability to capture complex prompts. To address these limitations, we propose a Regional Attention Guided Super-Resolution (RAGSR) method that explicitly extracts localized fine-grained information and effectively encodes it through a novel regional attention mechanism, enabling both enhanced detail and overall visually coherent SR results. Specifically, RAGSR localizes object regions in an image and assigns fine-grained caption to each region, which are formatted as region-text pairs as textual priors for T2I models. A regional guided attention is then leveraged to ensure that each region-text pair is properly considered in the attention process while preventing unwanted interactions between unrelated region-text pairs. By leveraging this attention mechanism, our approach offers finer control over the integration of text and image information, thereby effectively overcoming limitations faced by traditional SISR techniques. Experimental results on benchmark datasets demonstrate that our approach exhibits superior performance in generating perceptually authentic visual details while maintaining contextual consistency compared to existing approaches.