A Highly Scalable Parallel Algorithm for Isotropic Total Variation Models
Authors: Jie Wang, Qingyang Li, Sen Yang, Wei Fan, Peter Wonka, Jieping Ye
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate that our approach outperforms existing state-of-the-art algorithms, especially in dealing with high-resolution, megasize images. |
| Researcher Affiliation | Collaboration | 1Arizona State University, Tempe, AZ 85287 USA 2Huawei Noahs Ark Lab, Hong Kong, China 3King Abdullah University of Science and Technology, Thuwal, Saudi Arabia |
| Pseudocode | Yes | Algorithm 1 FAD (Fast ADMM for TV Models) |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-source code of their proposed FAD/pFAD method. |
| Open Datasets | No | The paper mentions using synthetic images and specific real images (e.g., moon image, MRI images) but does not provide concrete access information (link, DOI, repository, formal citation with authors/year for a public dataset) for these datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or test sets. |
| Hardware Specification | Yes | Thus, we implement p FAD with Open MP for this experiment and test all of the algorithms on a server with four quad-core (16 processors in total) Intel Xeon 2.93GHz CPUs and 65GB memory. |
| Software Dependencies | No | The paper mentions software like Open MP, MPI, MFISTA, Split Bregman, FCSA, and Matlab, but does not provide specific version numbers for these software components or other dependencies. |
| Experiment Setup | Yes | We keep γ fixed (γ = 10) in this paper. For schemes about varying γ, we refer readers to (Boyd et al., 2011). ... We set ϵ = 10 4. ... The regularization parameter λ is set to be 0.2. ... The regularization parameter λ of p FAD is set to be 0.001. ... We use the default settings of the FCSA package. The sample ratio is about 20% and Gaussian noise N(0, 0.012) is added to the undersamples to generate Y. ... We run both FCSA-p FAD and FCSA-MFISTA for 50 iterations. |