Flow-Filter: Plug-and-Play Image Smoothing with Flow Matching

Structure-aware image smoothing seeks to remove textures while preserving structures, but balancing texture suppression and structural fidelity remains challenging. Handcrafted priors offer explicit local geometric constraints yet risk over-smoothing, while pretrained generative priors capture rich natural-image statistics but may retain or reconstruct unwanted textures. To address this challenge, we propose Flow-Filter, a plug-and-play image smoothing framework that uses a pretrained Flow Matching model as a generative structural prior for structure–texture decomposition and requires no task-specific training or fine-tuning. We further introduce a joint gradient structural prior that couples first- and second-order differential responses, allowing global natural-image modeling from the generative prior to complement explicit local geometric constraints. To adapt FM to selective texture removal, we couple its denoising process with progressive ADMM-based decomposition and introduce per-step clean–noise endpoint modulation to reduce structural drift and persistent texture residuals. Extensive experiments demonstrate that Flow-Filter suppresses complex textures more effectively while preserving salient structures and fine geometric details, achieving a favorable balance between smoothing strength and structural fidelity. Our results show that pretrained Flow Matching models can go beyond their conventional role in generation and serve as effective plug-and-play priors for selective image filtering.