Motion Deblurring via Spatial-Temporal Collaboration of Frames and Events
Authors: Wen Yang, Jinjian Wu, Jupo Ma, Leida Li, Guangming Shi
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on both synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance. Project website: https://github.com/wyangvis/STCNet. |
| Researcher Affiliation | Academia | Wen Yang1,2, Jinjian Wu1,2 , Jupo Ma1,2 , Leida Li1, Guangming Shi1,2 1School of Artificial Intelligence, Xidian University, Xi an 710071, China 2Pazhou Lab, Huangpu, 510555, China wen.yang@stu.xidian.edu.cn, {jinjian.wu, majupo, ldli, gmshi}@xidian.edu.cn |
| Pseudocode | No | The paper illustrates its network architecture with figures but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Project website: https://github.com/wyangvis/STCNet. |
| Open Datasets | Yes | Our STCNet is evaluated on 1) Synthetic dataset. Go Pro (Nah, Hyun Kim, and Mu Lee 2017) and DVD (Su et al. 2017) datasets are widely adopted for image-only and event-based deblurring such as (Sun et al. 2022), which contains synthetic blurring images and sharp ground-truth images, as well as synthetic events generated by simulation algorithm ESIM (Rebecq, Gehrig, and Scaramuzza 2018). |
| Dataset Splits | No | For the REB dataset, "There are 60 videos of REB, 40 of which are used for training and 20 for testing." The paper does not explicitly mention a validation split for any of the datasets used, nor does it specify the standard splits for Go Pro or DVD datasets if they include a validation set. |
| Hardware Specification | Yes | Our method is implemented using Pytorch on NVIDIA RTX 3090 GPU. |
| Software Dependencies | No | The paper mentions 'Pytorch' but does not specify its version number, nor does it list other software dependencies with their respective versions. |
| Experiment Setup | Yes | The size of training patch is 256 256 with minibatch size of 8. The optimizer is ADAM (Kingma and Ba 2015), and the learning rate is initialized at 2 10 4 and decreased by the cosine learning rate strategy with a minimum learning rate of 10 6. For data augmentation, each patch is horizontally flipped with the probability of 0.5. |