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..
Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring
Authors: Jiangxin Dong, Stefan Roth, Bernt Schiele
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental results show that the proposed deep Wiener deconvolution network facilitates deblurred results with visibly fewer artifacts. Moreover, our approach quantitatively outperforms state-of-the-art non-blind image deblurring methods by a wide margin. 4 Experimental Results |
| Researcher Affiliation | Academia | Jiangxin Dong MPI Informatics EMAIL Stefan Roth TU Darmstadt EMAIL Bernt Schiele MPI Informatics EMAIL |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The PyTorch code and trained models are available at our Project page. |
| Open Datasets | Yes | We collect a training dataset including 400 images from the Berkeley segmentation [24] and 4744 images from the Waterloo Exploration [22] datasets. |
| Dataset Splits | No | The paper describes training and test datasets but does not explicitly provide details on a separate validation split or cross-validation methodology. It only mentions 'Test datasets' for evaluation. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, memory, or cloud instance types) used for experiments were provided in the paper. |
| Software Dependencies | No | The paper mentions 'PyTorch code' but does not provide specific version numbers for PyTorch or any other software libraries or dependencies. |
| Experiment Setup | Yes | Balancing effectiveness and efficiency, we use a total of two scales in the multi-scale feature refinement module. We empirically use M = 16 features and set γl = 1. For training the network parameters, we adopt the Adam optimizer [14] with default parameters. The batch size is set to 8. The learning rate is initialized as 10^-4, which is halved every 200 epochs. |