arXiv (All)
open access
Mixed-Noise Plug-and-Play with Infimal Convolution Fidelities and Multiple Priors
Plug-and-Play (PnP) algorithms are a class of iterative methods for inverse imaging. Within an optimization algorithm, they combine a flexible fidelity term, encoding the forward operator, and a pretrained image denoiser, in order to deal with more severe corruptions such as blurring or downsampling
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Convergent Plug-and-Play Image Restoration with Annealed Noise Levels#1116724
- Block-coordinate Plug-And-Play Methods with Armijo-like line-search for Image Restoration#974341
- Trajectory Stitching for Solving Inverse Problems with Flow-Based Models#1156274
- Jacobi-accelerated FFT-based solver for smooth high-contrast data#1106590
- Plug-and-Play Methods Provably Converge Even with Improperly Trained Denoisers: Convergence by Architectural Design#1117799
Record · ID 1118970
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive