Proximal Denoiser for Convergent Plug-and-Play Optimization with Nonconvex Regularization
Authors: Samuel Hurault, Arthur Leclaire, Nicolas Papadakis
ICML 2022 | Conference PDF | Archive PDF | Plain Text | 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 <samuel.hurault@math.u-bordeaux.fr>. |
| 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. |