arXiv Computer Vision
Unaligned Image Guided Denoising via Cross-modal Conditional Flow Matching
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MPFlow: Multi-modal Posterior-Guided Flow Matching for Zero-Shot MRI Reconstruction
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Improving Image-to-Image Translation via a Rectified Flow Reformulation
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MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration
MG-SpaIR is a training-data-free framework for restoring a clean image from a single observation corrupted by a mixture of blur, downsampling, noise, and missing pixels. Building on implicit neural representations (INRs), we introduce a multi-grade coarse-to-fine residual hierarchy that progressively refines the reconstruction across resolution grades, improving representational fidelity and mitigating spectral limitations.