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

• URL: http://arxiv.org/abs/2507.05604v1
• Date: Tue, 08 Jul 2025 02:33:44 GMT
• Title: Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restoration
• Authors: Yuyang Hu, Kangfu Mei, Mojtaba Sahraee-Ardakan, Ulugbek S. Kamilov, Peyman Milanfar, Mauricio Delbracio,
• Abstract summary: Kernel Density Steering (KDS) is a novel inference-time framework promoting robust, high-fidelity outputs through explicit local mode-seeking.<n>KDS employs an $N$-particle ensemble of diffusion samples, computing patch-wise kernel density estimation gradients from their collective outputs.<n>This collective local mode-seeking mechanism, acting as "collective wisdom", steers samples away from spurious modes prone to artifacts.
• Score: 23.098667686003928
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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