[Submitted on 30 Jun 2026]
Title:MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration
View a PDF of the paper titled MG-SpaIR: Multi-grade Sparse-guided Implicit Representation for Training-Data-Free Image Restoration, by Jianmin Liao and 5 other authors
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Abstract: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. To stabilize reconstruction optimization and suppress INR-induced artifacts, we further propose an explicit sparse proximal regularization (e.g., $\ell_0$-type) applied directly in the high-resolution image domain, which discourages spurious high-frequency patterns while preserving sharp structures. The resulting optimization is solved efficiently via a multi-grade proximal alternating scheme, and we establish convergence guarantees for the associated updates under standard regularity conditions. Experiments on mixed-degradation benchmarks demonstrate that MG-SpaIR consistently outperforms strong training-data-free baselines such as Deep Image Prior, providing a stable, interpretable, and data-efficient alternative to conventional learning-based restoration methods.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
MSC classes: 68U10, 94A08, 65K10, 68T07
ACM classes: I.4.4; I.4.5; G.1.6
Cite as: arXiv:2607.00138 [cs.CV]
(or arXiv:2607.00138v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.00138
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jianmin Liao [view email] [v1] Tue, 30 Jun 2026 20:17:37 UTC (25,886 KB)
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