Direction-aware Feature-level Frequency Decomposition for Single Image Deraining
Authors: Sen Deng, Yidan Feng, Mingqiang Wei, Haoran Xie, Yiping Chen, Jonathan Li, Xiao-Ping Zhang, Jing Qin
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We extensively evaluate the proposed approach in three representative datasets and experimental results corroborate our approach consistently outperforms state-of-the-art deraining algorithms. In this section, we evaluate our method on three synthetic datasets: Rain200L, Rain200H [Yang et al., 2017] and Rain800 [Zhang et al., 2019]. |
| Researcher Affiliation | Academia | 1Nanjing University of Aeronautics and Astronautics, Nanjing, China 2Lingnan University, Hong Kong, China 3Xiamen Univeristy, Xiamen, China 4Ryerson University, Toronto, Canada 5Hong Kong Polytechnic University, Hong Kong, China |
| Pseudocode | No | The paper describes the architecture and components verbally and visually (Figure 2) but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | There is no explicit statement about releasing the code for the described method, nor any links to a code repository. |
| Open Datasets | Yes | In this section, we evaluate our method on three synthetic datasets: Rain200L, Rain200H [Yang et al., 2017] and Rain800 [Zhang et al., 2019]. |
| Dataset Splits | No | The paper mentions using 'three synthetic datasets' but does not specify the train/validation/test splits, percentages, or absolute counts for any of them. It refers to 'evaluation' but not explicit split details. |
| Hardware Specification | No | The paper does not mention any specific hardware (GPU, CPU models, etc.) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | No | The paper discusses the network architecture and loss functions but does not provide specific training hyperparameters such as learning rate, batch size, number of epochs, or optimizer details. |