A geometry-based deep equilibrium model for image restoration under multiplicative Gamma noise

arXiv:2608.04944 · math.NA, cs.LG · Submitted 2026-08-05 · Read on arXiv

Shengkun Yang, Luca Ratti, Zhichang Guo

math.NA, cs.LG

Submitted: 2026-08-05

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Fidelity — 49× in this paper · explained in 2 episodes
• Regularization — 40× in this paper · explained in 3 episodes
• Influence Functions — 21× in this paper · explained in 3 episodes
• Inverse Problem — 14× in this paper · explained in 2 episodes
• Fixed Point — 12× in this paper · explained in 2 episodes
• Objective Function — 5× in this paper · explained in 2 episodes
• Parameterization — 5× in this paper · explained in 2 episodes
• Total Variation — 5× in this paper
• Convergence Guarantees — 4× in this paper · explained in 3 episodes
• Composition — 3× in this paper · explained in 2 episodes
• Interpretability — 3× in this paper · explained in 15 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 3 episodes
• Computational Efficiency — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Core — 2× in this paper · explained in 2 episodes
• Generalization — 2× in this paper · explained in 14 episodes
• Synthetic Aperture Radar — 2× in this paper
• Theoretical Guarantees — 2× in this paper · explained in 2 episodes
• Verification — 2× in this paper · explained in 2 episodes
• Clarity — 1× in this paper · explained in 3 episodes
• Geometric interpretation — 1× in this paper · explained in 2 episodes
• Learned Perceptual Image Patch Similarity — 1× in this paper
• Loss Function — 1× in this paper · explained in 2 episodes
• Parameter Efficiency — 1× in this paper · explained in 3 episodes
• Peak Signal-to-Noise Ratio — 1× in this paper
• Preconditioning — 1× in this paper · explained in 2 episodes
• Structural Fidelity — 1× in this paper · explained in 2 episodes

Sources

• A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution

Related papers

• Do physics-informed neural networks (PINNs) need to be deep? Shallow PINNs using the Levenberg-Marquardt algorithm
• A Neural-preconditioned Poisson Solver for Mixed Dirichlet and Neumann Boundary Conditions
• Advanced Linear Algebra with Applications - Part I (Numerical linear algebra for PDEs, machine learning, and data assimilation)
• Operator learning meets inverse problems: A probabilistic perspective
• Second-order consistency for learning chaotic dynamics via randomized Jacobian matching
• Windowed thinning and query complexity for the bouncy particle and Zigzag samplers