RGB-Event ISP: The Dataset and Benchmark
Authors: Yunfan LU, Yanlin Qian, Ziyang Rao, Junren Xiao, Liming Chen, Hui Xiong
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | First, we present a new event-RAW paired dataset, collected with a novel but still confidential sensor that records pixel-level aligned events and RAW images... Second, we propose a conventional ISP pipeline to generate good RGB frames as reference... Third, we classify the existing learnable ISP methods into 3 classes, and select multiple methods to train and evaluate on our new dataset. Lastly, since there is no prior work for reference, we propose a simple event-guided ISP method and test it on our dataset. |
| Researcher Affiliation | Collaboration | Yunfan Lu, Yanlin Qian, Ziyang Rao, Junren Xiao, Liming Chen2, Hui Xiong AI Thrust, Hong Kong University of Science and Technology (Guangzhou); Alpsen Tek2 EMAIL, EMAIL EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper describes the controllable ISP pipeline and proposed methods in prose and figures, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | In summary, to the best of our knowledge, this is the very first research focusing on event-guided ISP, and we hope it will inspire the community. The code and dataset are available at: https://github.com/yunfan Lu/RGB-Event-ISP. |
| Open Datasets | Yes | In summary, to the best of our knowledge, this is the very first research focusing on event-guided ISP, and we hope it will inspire the community. The code and dataset are available at: https://github.com/yunfan Lu/RGB-Event-ISP. |
| Dataset Splits | Yes | We divided the dataset into training and test sets, with 3/4 of the data used for training and 1/4 for testing. The testing set includes 3 indoor scenes and 3 outdoor scenes to ensure sufficient diversity. |
| Hardware Specification | Yes | Implementation Details: All our models were trained and tested on the same machine with a single A40 GPU with 48GB of GPU memory. |
| Software Dependencies | No | We used Py Torch (Paszke et al., 2017) for all experiments, applying random cropping and rotation for data augmentation. |
| Experiment Setup | Yes | The training batch size was 1, with each patch sized at 1024 1024. The learning rate was 0.0001, and all models were trained for 50 epochs. |