Abstract
Visual autoregressive (VAR) models have recently emerged as a promising alternative for image generation, offering stable training, non-iterative inference, and high-fidelity synthesis through next-scale prediction. This encourages the exploration of VAR for image super-resolution (ISR), yet its application remains underexplored and faces two critical challenges: locality-biased attention, which fragments spatial structures, and residual-only supervision, which accumulates errors across scales, severely compromises global consistency of reconstructed images. To address these issues, we propose AlignVAR, a globally consistent visual autoregressive framework tailored for ISR, featuring two key components: (1) Spatial Consistency Autoregression (SCA), which applies an adaptive mask to reweight attention toward structurally correlated regions, thereby mitigating excessive locality and enhancing long-range dependencies; and (2) Hierarchical Consistency Constraint (HCC), which augments residual learning with full reconstruction supervision at each scale, exposing accumulated deviations early and stabilizing the coarse-to-fine refinement process. Extensive experiments demonstrate that AlignVAR consistently enhances structural coherence and perceptual fidelity over existing generative methods, while delivering over 10x faster inference with nearly 50% fewer parameters than leading diffusion-based approaches, establishing a new paradigm for efficient ISR.
Abstract (translated)
最近，视觉自回归（VAR）模型作为图像生成的一个有前景的替代方案出现，通过下一尺度预测提供了稳定的训练、非迭代推理和高保真合成。这激发了将VAR应用于超分辨率图像恢复（ISR）的研究兴趣，尽管其应用仍然有限，并面临两大关键挑战：局部偏置注意机制，它会破坏空间结构；以及仅限残差监督，这会导致跨尺度累积误差，严重损害重建图像的整体一致性。为了解决这些问题，我们提出了AlignVAR，这是一种专为ISR设计的全局一致视觉自回归框架，具有两个主要组件： 1. **空间一致性自回归（SCA）**：该机制应用了一个自适应掩码来重新加权注意权重以偏重于结构相关的区域，从而缓解过度局部化的现象，并增强长距离依赖关系。 2. **层级一致性约束（HCC）**：该方法通过在每个尺度上添加完全重建监督来补充残差学习，这有助于及早暴露累积的偏差并稳定从粗到细的细化过程。 广泛的实验表明，与现有的生成方法相比，AlignVAR在结构一致性和感知保真度方面始终具有显著优势。此外，它还提供了超过10倍更快的推理速度，并且参数量比领先的基于扩散的方法减少了近50%，从而为高效ISR建立了一种新的范式。
URL
https://arxiv.org/abs/2603.00589
