InfScene-SR: Spatially Continuous Inference for Arbitrary-Size Image Super-Resolution

• URL: http://arxiv.org/abs/2602.19736v1
• Date: Mon, 23 Feb 2026 11:34:59 GMT
• Title: InfScene-SR: Spatially Continuous Inference for Arbitrary-Size Image Super-Resolution
• Authors: Shoukun Sun, Zhe Wang, Xiang Que, Jiyin Zhang, Xiaogang Ma,
• Abstract summary: InfScene-SR is a framework enabling spatially continuous super-resolution for large, arbitrary scenes.<n>We adapt the iterative refinement process of diffusion models with a novel guided and variance-corrected fusion mechanism.
• Score: 3.6762434952581713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image Super-Resolution (SR) aims to recover high-resolution (HR) details from low-resolution (LR) inputs, a task where Denoising Diffusion Probabilistic Models (DDPMs) have recently shown superior performance compared to Generative Adversarial Networks (GANs) based approaches. However, standard diffusion-based SR models, such as SR3, are typically trained on fixed-size patches and struggle to scale to arbitrary-sized images due to memory constraints. Applying these models via independent patch processing leads to visible seams and inconsistent textures across boundaries. In this paper, we propose InfScene-SR, a framework enabling spatially continuous super-resolution for large, arbitrary scenes. We adapt the iterative refinement process of diffusion models with a novel guided and variance-corrected fusion mechanism, allowing for the seamless generation of large-scale high-resolution imagery without retraining. We validate our approach on remote sensing datasets, demonstrating that InfScene-SR not only reconstructs fine details with high perceptual quality but also eliminates boundary artifacts, benefiting downstream tasks such as semantic segmentation.

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