Denoising and Completion of 3D Data via Multidimensional Dictionary Learning
Authors: Zemin Zhang, Shuchin Aeron
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 5 we show experiment results on third order tensor completion and denoising. and 4 Experiment Results 4.1 Filling Missing Pixels in Tensors 4.2 Multispectral Image and Video Denoising |
| Researcher Affiliation | Academia | Zemin Zhang and Shuchin Aeron Department of Electrical and Computer Engineering, Tufts University |
| Pseudocode | Yes | Algorithm 1 T-SVD of third order tensors and Algorithm 2 K-TSVD |
| Open Source Code | No | The paper does not include an explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | The multispectral data was from the Columbia datasets 1, each dataset contains 31 real-world images of size 512 512 and is collected from 400nm to 700nm at 10nm steps... 1http://www1.cs.columbia.edu/CAVE/databases/multispectral/ |
| Dataset Splits | No | The paper mentions splitting data into training and testing sets for experiments (e.g., 'first 30 frames' for training and 'last 10 frames' for testing for the basketball video), but it does not specify a validation set or explicit train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models, or specific cloud/cluster configurations used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | Input : Observed tensor data Y = { ! Y i}n2 i=1 2 Rn1 n2 n3, λ > 0. Initialize: Dictionary D0 2 Rn1 K n3 Repeat until convergence... We randomly took 9000 overlapping block patches of size 8 8 10 from the first 30 frames... All these patches were used to train a tensor dictionary with K = 256 atoms. |