Scale Adaptive Blind Deblurring
Authors: Haichao Zhang, Jianchao Yang
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | 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. |