发表机构
Sun Yat-Sen University; Beijing Institute of Technology（中山大学; 北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生成式超分辨率的幻觉问题，提出K2N将全路径自回归生改为k到N的细节延续，直接由LR建立粗尺度状态，仅自回归恢复精细尺度，在标准指标上与VARSR相当，幻觉评估中优势明显。
AI 中文摘要
幻觉是生成式超分辨率（GSR）中持续存在的挑战，针对低分辨率（LR）输入，重建结果可能包含视觉上看似合理但支撑不足的内容、结构偏差或不自然的纹理。现有GSR方法已广泛探索感知真实性与重建保真度之间的权衡，但在整体重建过程中，可靠粗尺度信息的保留与更不确定的精细细节恢复之间的划分往往被隐式处理。视觉自回归（VAR）建模为此问题提供了重新审视的自然契机，因其由粗到细的下一级预测提供了显式的按尺度生成接口。然而，现有基于VAR的超分辨率方法仍沿用原始的全1到N自回归生成路径，尽管对于超分辨率而言，LR中的粗尺度信息通常相对更可靠，但长自回归链可能会累积预测误差。受这些观察启发，我们提出K2N，它将基于VAR的超分辨率从全路径生成重新表述为k到N的细节延续过程。具体而言，早期粗尺度状态直接由LR建立，而仅剩余的更精细尺度以自回归方式恢复。实验结果表明，K2N在标准超分辨率指标上与VARSR基线保持竞争力，而在聚焦幻觉的评估中表现出明显优势。这些发现表明，以按尺度的方式重新思考生成路径，可成为提升生成式超分辨率可靠性的有前景方向。我们的代码很快将在该httpsURL发布。
英文摘要
Hallucination remains a persistent challenge in generative super-resolution (GSR), where reconstructed results may contain visually plausible yet weakly supported content, structural deviations, or unnatural textures with respect to the low-resolution (LR) input. Existing GSR methods have extensively explored the trade-off between perceptual realism and reconstruction fidelity, but the division between preserving reliable coarse-scale information and restoring more uncertain fine details is often handled implicitly within the overall restoration process. Visual autoregressive (VAR) modeling provides a natural opportunity to revisit this issue, as its coarse-to-fine next-scale prediction offers an explicit scale-wise generation interface. However, existing VAR-based SR methods still inherit the original full 1-to-$N$ autoregressive generation path, even though, for super-resolution, coarse-scale information in LR is often relatively more reliable, while long autoregressive chains may accumulate prediction errors. Motivated by these observations, we propose \textbf{K2N}, which reformulates VAR-based SR from full-path generation into a $k$-to-$N$ detail continuation process. Specifically, early coarse-scale states are established directly from LR, while only the remaining finer scales are restored autoregressively. Experimental results show that K2N remains competitive with the VARSR baseline on standard SR metrics, while exhibiting clearer advantages on hallucination-focused evaluation. These findings suggest that explicitly rethinking the generation path in a scale-wise manner can be a promising direction for improving the reliability of generative super-resolution. Our code will be released soon at https://github.com/BRL-SYSU/K2NSR.
CommentsAccepted by ACM Multimedia 2026
