Local-Global Context-Aware and Structure-Preserving Image Super-Resolution

• URL: http://arxiv.org/abs/2510.13649v1
• Date: Sat, 11 Oct 2025 07:17:31 GMT
• Title: Local-Global Context-Aware and Structure-Preserving Image Super-Resolution
• Authors: Sanchar Palit, Subhasis Chaudhuri, Biplab Banerjee,
• Abstract summary: Pretrained text-to-image models, such as Stable Diffusion, have exhibited strong capabilities in synthesizing realistic image content.<n>We propose a contextually precise image super-resolution framework that effectively maintains both local and global pixel relationships.
• Score: 23.87231269881077
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have recently achieved significant success in various image manipulation tasks, including image super-resolution and perceptual quality enhancement. Pretrained text-to-image models, such as Stable Diffusion, have exhibited strong capabilities in synthesizing realistic image content, which makes them particularly attractive for addressing super-resolution tasks. While some existing approaches leverage these models to achieve state-of-the-art results, they often struggle when applied to diverse and highly degraded images, leading to noise amplification or incorrect content generation. To address these limitations, we propose a contextually precise image super-resolution framework that effectively maintains both local and global pixel relationships through Local-Global Context-Aware Attention, enabling the generation of high-quality images. Furthermore, we propose a distribution- and perceptual-aligned conditioning mechanism in the pixel space to enhance perceptual fidelity. This mechanism captures fine-grained pixel-level representations while progressively preserving and refining structural information, transitioning from local content details to the global structural composition. During inference, our method generates high-quality images that are structurally consistent with the original content, mitigating artifacts and ensuring realistic detail restoration. Extensive experiments on multiple super-resolution benchmarks demonstrate the effectiveness of our approach in producing high-fidelity, perceptually accurate reconstructions.

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