Stabilizing Deep Reconstruction Operators with Contractive Anchoring
Pretrained deep denoisers can be used to solve a wide range of model-based image reconstruction tasks via Plug-and-Play (PnP) and Regularization-by-Denoising (RED) algorithms, without retraining per task. These denoisers are trained only for single-step denoising. Using them as Image Reconstruction (IR) regularizers in an iterative process can destabilize reconstruction. A common failure mode is the peak-and-collapse behaviour: metrics such as PSNR improve for early iterations and then abruptly degrade, making these algorithms unreliable in practice. We propose a data-driven stabilization framework that (i) formalizes this instability of any IR operator through a local quantity and (ii) p...
| Authors | Arghya Sinha, Trishit Mukherjee, Kunal N. Chaudhury |
|---|---|
| Primary category | eess.IV |
| Published | 25 July 2026 |
| Last updated at source | 29 July 2026 |
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