Convergent Bregman Plug-and-Play Image Restoration for Poisson Inverse Problems
Authors: Samuel Hurault, Ulugbek Kamilov, Arthur Leclaire, Nicolas Papadakis
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental evaluations conducted on various Poisson inverse problems validate the convergence results and showcase effective restoration performance. |
| Researcher Affiliation | Academia | Samuel Hurault Univ. Bordeaux, CNRS, INRIA, Bordeaux INP, IMB, UMR 5251 samuel.hurault@math.u-bordeaux.fr Ulugbek Kamilov Washington University in St. Louis kamilov@wustl.edu Arthur Leclaire Univ. Bordeaux, CNRS, INRIA, Bordeaux INP, IMB, UMR 5251 LTCI, Télécom Paris, IP Paris arthur.leclaire@telecom-paris.fr Nicolas Papadakis Univ. Bordeaux, CNRS, INRIA, Bordeaux INP, IMB, UMR 5251 nicolas.papadakis@math.u-bordeaux.fr |
| Pseudocode | No | The paper describes algorithms using mathematical equations and textual explanations (e.g., equations (18) and (20)), but it does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We use the same training dataset as [Zhang et al., 2021]." and "Average denoising PSNR performance of Inverse Gamma noise denoisers B-DRUNet and DRUNet on 256 × 256 center-cropped images from the CBSD68 dataset, for various noise levels γ. |
| Dataset Splits | No | The paper states that hyperparameters are optimized by grid search, and refers to using the same training dataset as [Zhang et al., 2021], but does not explicitly provide the training/validation/test dataset splits used in their own experiments. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware (e.g., GPU models, CPU types, memory details) used to run its experiments. |
| Software Dependencies | No | The paper mentions using the DRUNet architecture and ADAM optimizer but does not specify versions for core software dependencies like programming languages, deep learning frameworks (e.g., PyTorch, TensorFlow), or CUDA. |
| Experiment Setup | Yes | Training is performed with ADAM during 1200 epochs. The learning rate is initialized with learning rate 10−4 and is divided by 2 at epochs 300, 600 and 900. The algorithm terminates when the relative difference between consecutive values of the objective function is less than 10−8 or the number of iterations exceeds K = 500. The hyper-parameters γ, λ are optimized for each algorithm and for each noise level α by grid search. Initialization is done with x0 = ATy. Table 2: B-RED and B-Pn P hyperparameters |