Unfolding the Alternating Optimization for Blind Super Resolution
Authors: zhengxiong luo, Yan Huang, Shang Li, Liang Wang, Tieniu Tan
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on synthetic datasets and real-world images show that our model can largely outperform state-of-the-art methods and produce more visually favorable results at much higher speed. |
| Researcher Affiliation | Academia | Zhengxiong Luo1,2,3, Yan Huang1,2, Shang Li2,3, Liang Wang1,4,5, and Tieniu Tan1,4 1 Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) 2 Institute of Automation, Chinese Academy of Sciences (CASIA) 3 School of Artificial Intelligence, University of Chinese Academy of Sciences (UCAS) 4 Center for Excellence in Brain Science and Intelligence Technology (CEBSIT) 5 Chinese Academy of Sciences, Artificial Intelligence Research (CAS-AIR) |
| Pseudocode | No | The paper provides architectural diagrams of the network modules but no pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is available at https://github.com/greatlog/DAN.git. |
| Open Datasets | Yes | We collect 3450 HR images from DIV2K [1] and Flickr2K [11] as training set. |
| Dataset Splits | No | The paper details training and testing data but does not explicitly provide information on validation set splits or its usage in the training process. |
| Hardware Specification | Yes | All models are trained on RTX2080Ti GPUs. |
| Software Dependencies | No | The paper mentions software like Adam for optimization but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The input size during training is 64 64 for all scale factors. The batch size is 64. Each model is trained for 4 10^5 iterations. We use Adam [22] as our optimizer, with β1 = 0.9, β2 = 0.99. The initial learning rate is 2 10^4, and will decay by half at every 1 10^5 iterations. |