ReMoNet: Recurrent Multi-Output Network for Efficient Video Denoising
Authors: Liuyu Xiang, Jundong Zhou, Jirui Liu, Zerun Wang, Haidong Huang, Jie Hu, Jungong Han, Yuchen Guo, Guiguang Ding2786-2794
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments on NVIDIA GPU and Qualcomm Snapdragon 888 mobile platform with Gaussian noise and simulated Image Signal-Processor (ISP) noise. The experimental results show that Re Mo Net is both effective and efficient on video denoising. |
| Researcher Affiliation | Collaboration | 1 Beijing National Research Center for Information Science and Technology (BNRist) 2 School of Software, Tsinghua University, Beijing, China 3 OPPO Inc, Guangdong, China. 4 Computer Science Department, Aberystwyth University, SY23 3FL, UK |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | We use two benchmark datasets for evaluation: Set8 and DAVIS-test. Set8 consists of 4 sequences captured by GOPRO camera and 4 sequences from the Derf s Test Media collection, with a resolution of 960 × 540. The DAVIS-test contains 30 sequences of resolution 854 × 480. We use DAVIS-train for training. |
| Dataset Splits | Yes | We use two benchmark datasets for evaluation: Set8 and DAVIS-test. [...] We use DAVIS-train for training. |
| Hardware Specification | Yes | We conduct extensive experiments on NVIDIA GPU and Qualcomm Snapdragon 888 mobile platform with Gaussian noise and simulated Image Signal-Processor (ISP) noise. The experiments are conducted on the Qualcomm Snapdragon 888 mobile platform, where its GPU is used for inference. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | In practice, we choose the input number of frames 2T + 1 = 5 and temporal fusion size 2K + 1 = 3, the RTF hidden dimension L = 32. |