Generative Status Estimation and Information Decoupling for Image Rain Removal
Authors: Di Lin, Xin WANG, Jia Shen, Renjie Zhang, Ruonan Liu, Miaohui Wang, Wuyuan Xie, Qing Guo, Ping Li
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate SEIDNet on the public datasets, achieving state-of-the-art performances of image rain removal. The experimental results also demonstrate the generalization of SEIDNet, which can be easily extended to achieve state-of-the-art performances on other image restoration tasks (e.g., snow, haze, and shadow removal). |
| Researcher Affiliation | Academia | Di Lin1, , Xin Wang2, , Jia Shen1, Renjie Zhang2, Ruonan Liu1, Miaohui Wang3, Wuyuan Xie3, Qing Guo4, and Ping Li2,* 1Tianjin University, China 2The Hong Kong Polytechnic University, Hong Kong 3Shenzhen University, China 4Center for Frontier AI Research, A*STAR, Singapore |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the implementation of SEIDNet via https://github.com/wxxx1025/SEIDNet. |
| Open Datasets | Yes | We evaluate SEIDNet on the public datasets for rain removal (i.e., Rain100H [16], Rain100L [16], Rain1400 [19], Rain13K [10] and SPA [3]), achieving state-of-the-art performances. |
| Dataset Splits | No | The paper provides training and testing split sizes for datasets (e.g., '1, 800/200/12, 600/638, 492 images for training, along with 100/100/1, 400/1, 000 images for testing.') but does not explicitly mention a validation set split. |
| Hardware Specification | No | The main paper does not explicitly describe the specific hardware (e.g., GPU/CPU models) used for running its experiments. The checklist refers to supplementary material for this information, which is not provided in the current text. |
| Software Dependencies | No | The main paper does not provide specific software dependencies with version numbers. The checklist refers to supplementary material for implementation details, which is not provided in the current text. |
| Experiment Setup | Yes | We define α = 4 as the weight of KL divergence. L2-norm and KL divergence compose the status estimation loss Lse as: Lse = L2(R, R ) + αKL(G(µr, σr) || G(µf, σf)). |