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..
Scale Adaptive Blind Deblurring
Authors: Haichao Zhang, Jianchao Yang
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments are carried out in Section 5, and the results are compared with those of the state-of-the-art methods in the literature.We perform extensive experiments in this section to evaluate the performance of the proposed method compared with several state-of-the-art blind deblurring methods, including two recent noise robust deblurring methods of Tai et al. [17], and Zhong et al. [26], as well as a non-uniform deblurring method of Xu et al. [23]. |
| Researcher Affiliation | Collaboration | Haichao Zhang Jianchao Yang Duke University, NC Adobe Research, CA |
| Pseudocode | No | The paper describes the proposed method using mathematical formulations and textual explanations, but does not include any explicit pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Evaluation using the Benchmark Dataset of Levin et al. [14]. We first perform evaluation on the benchmark dataset of Levin et al. [14], containing 4 images and 8 blur kernels, leading to 32 blurry images in total (see Figure 4). |
| Dataset Splits | No | The paper mentions using the benchmark dataset of Levin et al. [14] for evaluation, but does not explicitly provide details about training, validation, or test data splits, nor does it refer to standard predefined splits for this dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU or CPU models. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | We construct {f p} as Gaussian filters, with the radius uniformly sampled over a specified range, which is typically set as [0.1, 3] in the experiment. The number of iterations is used as the stopping criteria and is fixed as 15 in practice. |