High-Order Residual Network for Light Field Super-Resolution
Authors: Nan Meng, Xiaofei Wu, Jianzhuang Liu, Edmund Lam11757-11764
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our approach enables high-quality reconstruction even in challenging regions and outperforms state-of-the-art single image or LF reconstruction methods with both quantitative measurements and visual evaluation. |
| Researcher Affiliation | Collaboration | Nan Meng,1 Xiaofei Wu,2 Jianzhuang Liu,2 Edmund Y. Lam1 1Dept. of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong 2Huawei Noah s Ark Lab, China |
| Pseudocode | Yes | Algorithm 1: Aperture group batch normalization |
| Open Source Code | Yes | The entire implementation is available at https://github.com/monaen/Light Field Reconstruction. |
| Open Datasets | Yes | In the experiments, we randomly select 100 scenes from the Lytro Archive (Stanford) (excluding Occlusions and Reflective ) and the entire Fraunhofer densely-sampled highresolution (Ziegler et al. 2017) datasets for training. |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, or detailed methodology) for validation data. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | During training, the system each time receives a 4D patch of LF, which is spatially cropped to 96 x 96 as input. For spatial SR, the downsampling is based on the classical model (Farrugia and Guillemot 2018) ... The network is trained using the Stochastic Gradient Descent solver with the initial learning rate of 10 5, which is decreased by a factor of 0.1 for every 10 epochs. |