A geometry-based deep equilibrium model for image restoration under multiplicative Gamma noise
We propose a deep learning framework for image restoration from images degraded by both multiplicative Gamma noise and blur. Unlike conventional deep equilibrium (DEQ) models that rely on implicit neural regularization, the proposed method learns an explicit and interpretable regularizer parameterized by geometric priors associated with surface area and mean curvature. To minimize the resulting variational model, we develop a mirror descent algorithm tailored to the commonly used Gamma-noise fidelity terms. Leveraging the Kurdyka-Lojasiewicz property for functions defined in $o$-minimal structures, we establish the global convergence of the generated iterates to a critical point. Experime...
| Authors | Shengkun Yang, Luca Ratti, Zhichang Guo |
|---|---|
| Primary category | math.NA |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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