Image Formation Model Guided Deep Image Super-Resolution
Authors: Jinshan Pan, Yang Liu, Deqing Sun, Jimmy Ren, Ming-Ming Cheng, Jian Yang, Jinhui Tang11807-11814
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results show that the proposed algorithm performs favorably against state-of-the-art methods. |
| Researcher Affiliation | Collaboration | 1Nanjing University of Science and Technology, 2Dalian University of Technology, 3Google, 4Sense Time Research, 5Nankai University |
| Pseudocode | No | The paper describes the algorithm in text and with a diagram, but does not include pseudocode or an algorithm block. |
| Open Source Code | Yes | The code and trained models are publicly available on the authors websites. |
| Open Datasets | Yes | to generate LR images using bicubic downsampling from the DIV2K dataset (Timofte et al. 2017) for training and use the Set5 (Bevilacqua et al. 2012) as the validation test set. |
| Dataset Splits | Yes | and use the Set5 (Bevilacqua et al. 2012) as the validation test set. |
| Hardware Specification | Yes | The testing environment is on a machine with an Intel Core i7-7700 CPU and an NVIDIA GTX 1080Ti GPU. |
| Software Dependencies | No | The paper mentions 'Py Torch' but does not specify a version number or other software dependencies with version details. |
| Experiment Setup | Yes | In the learning process, we use the ADAM optimizer (Kingma and Ba 2014) with parameters β1 = 0.9, β2 = 0.999, and ϵ = 10 8. The minibatch size is set to be 1. The learning rate is initialized to be 10 4. We use a Gaussian kernel in (3) with the same settings used in (Shan et al. 2008). We empirically set T = 3 as a trade-off between accuracy and speed. |