Abstract
Diffusion models have become a leading paradigm for image super-resolution (SR), but existing methods struggle to guarantee both the high-frequency perceptual quality and the low-frequency structural fidelity of generated images. Although inference-time scaling can theoretically improve this trade-off by allocating more computation, existing strategies remain suboptimal: reward-driven particle optimization often causes perceptual over-smoothing, while optimal-path search tends to lose structural consistency. To overcome these difficulties, we propose Iterative Diffusion Inference-Time Scaling with Adaptive Frequency Steering (IAFS), a training-free framework that jointly leverages iterative refinement and frequency-aware particle fusion. IAFS addresses the challenge of balancing perceptual quality and structural fidelity by progressively refining the generated image through iterative correction of structural deviations. Simultaneously, it ensures effective frequency fusion by adaptively integrating high-frequency perceptual cues with low-frequency structural information, allowing for a more accurate and balanced reconstruction across different image details. Extensive experiments across multiple diffusion-based SR models show that IAFS effectively resolves the perception-fidelity conflict, yielding consistently improved perceptual detail and structural accuracy, and outperforming existing inference-time scaling methods.
Abstract (translated)
扩散模型已成为图像超分辨率（SR）的主导范式，但现有方法难以同时保证生成图像的高频感知质量和低频结构保真度。尽管推理时间缩放理论上可以通过分配更多计算来改善这种权衡，现有的策略仍然不够理想：基于奖励驱动的粒子优化往往导致感知过度平滑，而最优路径搜索则容易失去结构一致性。为克服这些困难，我们提出了迭代扩散推理时间缩放与自适应频率控制（IAFS），这是一种无需训练的框架，结合了迭代细化和频域感知粒子融合。 IAFS通过逐步修正生成图像中的结构性偏差来解决平衡感知质量和结构保真度的挑战。同时，它通过自适应地将高频感知线索与低频结构信息相结合，确保有效的频率融合，从而实现不同细节下的更准确且均衡的重建。 多项跨多个基于扩散模型的SR模型的实验表明，IAFS有效地解决了感知-保真度冲突问题，使得生成图像在感知细节和结构准确性方面均得到一致改善，并超越了现有的推理时间缩放方法。
URL
https://arxiv.org/abs/2512.23532
