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..
Unsupervised Image Denoising with Score Function
Authors: Yutong Xie, Mingze Yuan, Bin Dong, Quanzheng Li
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiment, Table 2: Quantitative comparison for various parameters of Σ in additive Gaussian noise using different methods in terms of PNSR (d B)/SSIM. |
| Researcher Affiliation | Academia | Yutong Xie Peking University, Mingze Yuan Peking University, Bin Dong Peking University, Quanzheng Li Massachusetts General Hospital and Harvard Medical School |
| Pseudocode | Yes | Algorithm 1 The general denoising process, Algorithm 2 An iterative trick to solve x = Σ (x) s (y) + y, Algorithm 3 An iterative method to solve Eq. 7 in the case of Rayleigh noise, Algorithm 4 The general framework to solve Eq. 7 for correlated multiplicative noise model, Algorithm 5 The full denoising process for mixture noise y = z + ϵ |
| Open Source Code | No | The paper mentions implementation details but does not provide an explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | DIV2K [22] and CBSD500 dataset [3] are used as training datasets. and Kodak dataset, CBSD68 [15] and CSet9. |
| Dataset Splits | No | The paper mentions using DIV2K and CBSD500 for training and Kodak, CBSD68, CSet9 for evaluation, but it does not specify explicit train/validation/test split percentages, sample counts, or a detailed splitting methodology for these datasets, nor does it explicitly mention a validation set split. |
| Hardware Specification | Yes | All the models are implemented in Py Torch [18] with NVidia V100. |
| Software Dependencies | No | The paper mentions 'Py Torch [18]' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | When training, we randomly clip the training images to patches with the resolution of 128 128. Adam W optimizer [14] is used to train the network. We train each model for 5000 steps with the batch size of 32. The learning rate is initialized to 1 10 4 for first 4000 steps and it is decreased to 1 10 5 for final 1000 steps. When an iterative algorithm is needed to solve Eq. 7, we set the number of iterations as 10. |