VPRP: Visual Prompt Restoration Pipeline for High-Quality Nighttime Flare Removal

Lens flare removal is essential for restoring images degraded by unwanted light scattering or reflection, yet existing methods frequently introduce artifacts in restored outputs. End-to-end approaches struggle with severe degradations where flare completely occludes underlying content, while conventional coarse-to-fine strategies yield marginal improvements when applied directly to flare removal. We identify that the core limitation lies in the lack of explicit guidance for identifying and restoring flare-corrupted regions. To address this, we propose the Visual Prompt Restoration Pipeline (VPRP), a model-agnostic framework that redefines coarse-to-fine processing through a plug-and-play Prompt Restoration Network (PRN). VPRP introduces two synergistic mechanisms: (1) a mask block that leverages predicted flare from the coarse stage to explicitly exclude corrupted pixels, enabling context-aware restoration, and (2) a prompt calibration block that repurposes high-quality decoder features as visual prompts to guide the refinement network, ensuring structural consistency. VPRP seamlessly integrates with arbitrary restoration architectures while introducing negligible computational overhead (minimum 1% parameter increase). Experiments demonstrate that VPRP significantly reduces artifacts and achieves state-of-the-art performance across multiple benchmarks, with PSNR improvements of up to 0.92 dB over strong baselines.