Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Authors: Samuel Hurault, Arthur Leclaire, Nicolas Papadakis
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These convergence results are confirmed with numerical experiments on deblurring, super-resolution and inpainting. |
| Researcher Affiliation | Academia | 1Univ. Bordeaux, Bordeaux INP, CNRS, IMB, UMR 5251,F33400 Talence, France. Correspondence to: Samuel Hurault <EMAIL>. |
| Pseudocode | Yes | The iterative algorithms Pn P-PGD (8), Pn P-ADMM (10), and Pn P-DRS (11, 12, 14) are presented with structured mathematical equations that define the steps of the procedure. |
| Open Source Code | Yes | 1Code is available at https://github.com/ samuro95/Prox-Pn P. |
| Open Datasets | Yes | Average denoising PSNR performance of our proxdenoiser and compared methods on 256x256 center-cropped images from the CBSD68 dataset (Martin et al., 2001), for various noise levels σ. |
| Dataset Splits | No | The paper mentions using the CBSD68 dataset and training/testing, but does not explicitly provide the percentages or specific counts for the training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | The algorithm terminates when the relative difference between consecutive values of the objective function is less than ϵ = 10^-8 or the number of iterations exceeds K = 1000. We will use for evaluation Gaussian noise with 3 noise levels ν ∈ {2.55, 7.65, 12.75}/255 i.e. ν ∈ {0.01, 0.03, 0.05}. For each noise level, we propose default values for the parameters σ and λ that we keep for both deblurring and super-resolution. These values are explicitly given in Appendix G.2. Table 7: Choice of parameters (λ, σ) for both debluring and super-resolution experiments of Section 5.2. These parameters are only scaled with respect to the input noise level ν. |