标题： 核密度引导：通过模式搜索进行图像修复的推理时间缩放 显示英文标题

标题： Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restoration

摘要： 扩散模型在图像恢复方面显示出前景，但现有方法常常在一致性和不良伪影方面遇到困难。 为了解决这个问题，我们引入了核密度引导（KDS），这是一种新颖的推理时间框架，通过显式局部模式搜索促进稳健、高保真输出。 KDS使用一个$N$粒子集合的扩散样本，从它们的集体输出中计算逐块核密度估计梯度。 这些梯度将每个粒子中的块引导至集合中识别出的共享、更高密度区域。 这种集体局部模式搜索机制，作为“集体智慧”，将样本从容易产生伪影的虚假模式中引导出来，这些模式可能由独立采样或模型不完善引起，并引导到更稳健、高保真的结构中。 这使我们能够在消耗更高计算资源的同时同时采样多个粒子，从而获得更高质量的样本。 作为一个即插即用的框架，KDS无需重新训练或外部验证器，可无缝集成到各种扩散采样器中。 广泛的数值验证表明，KDS在具有挑战性的现实世界超分辨率和图像修复任务中显著提高了定量和定性性能。 显示英文摘要

摘要： Diffusion models show promise for image restoration, but existing methods often struggle with inconsistent fidelity and undesirable artifacts. To address this, we introduce Kernel Density Steering (KDS), a novel inference-time framework promoting robust, high-fidelity outputs through explicit local mode-seeking. KDS employs an $N$-particle ensemble of diffusion samples, computing patch-wise kernel density estimation gradients from their collective outputs. These gradients steer patches in each particle towards shared, higher-density regions identified within the ensemble. This collective local mode-seeking mechanism, acting as "collective wisdom", steers samples away from spurious modes prone to artifacts, arising from independent sampling or model imperfections, and towards more robust, high-fidelity structures. This allows us to obtain better quality samples at the expense of higher compute by simultaneously sampling multiple particles. As a plug-and-play framework, KDS requires no retraining or external verifiers, seamlessly integrating with various diffusion samplers. Extensive numerical validations demonstrate KDS substantially improves both quantitative and qualitative performance on challenging real-world super-resolution and image inpainting tasks.