Functional Neural Networks for Parametric Image Restoration Problems
Authors: Fangzhou Luo, Xiaolin Wu, Yanhui Guo
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Section 4, extensive experiments are conducted to evaluate Func Nets on three parametric image restoration tasks. |
| Researcher Affiliation | Academia | Fangzhou Luo Mc Master University luof1@mcmaster.ca Xiaolin Wu Mc Master University xwu@mcmaster.ca Yanhui Guo Mc Master University guoy143@mcmaster.ca |
| Pseudocode | No | The paper describes the methods in text but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a repository. |
| Open Datasets | Yes | Following [67, 23, 60], we use the DIV2K dataset [49] for training. |
| Dataset Splits | Yes | There are 1000 high-quality images in the DIV2K dataset, 800 images for training, 100 images for validation and 100 images for testing. |
| Hardware Specification | No | All experiments run in parallel on 4 GPUs. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, specific library versions) are provided. |
| Experiment Setup | Yes | All our three Func Net models are trained by ADAM optimizor with β1 = 0.9, β2 = 0.999, and ϵ = 10 8. The initial learning rate is set to 10 4 and then decreases by half for every 2 105 iterations of back-propagation. In the super-resolution problem, we randomly extract 32 LR RGB patches with the size of 40 40 as a batch input. In the image denoising problem, we randomly extract 32 RGB patches with the size of 96 96 as a batch input. In the JPEG deblocking problem, we randomly extract 32 gray patches with the size of 96 96 as a batch input |