Hybrid Event-Based Vision Sensor (HybridEVS) is a novel sensor integrating traditional frame-based and event-based sensors, offering substantial benefits for applications requiring low-light, high dynamic range, and low-latency environments, such as smartphones and wearable devices. Despite its potential, the lack of Image signal processing (ISP) pipeline specifically designed for HybridEVS poses a significant challenge. To address this challenge, in this study, we propose a coarse-to-fine framework named DemosaicFormer which comprises coarse demosaicing and pixel correction. Coarse demosaicing network is designed to produce a preliminary high-quality estimate of the RGB image from the HybridEVS raw data while the pixel correction network enhances the performance of image restoration and mitigates the impact of defective pixels. Our key innovation is the design of a Multi-Scale Gating Module (MSGM) applying the integration of cross-scale features, which allows feature information to flow between different scales. Additionally, the adoption of progressive training and data augmentation strategies further improves model’s robustness and effectiveness. Experimental results show superior performance against the existing methods both qualitatively and visually, and our DemosaicFormer achieves the best performance in terms of all the evaluation metrics in the MIPI 2024 challenge on Demosaic for Hybridevs Camera.
This repository contains the official implementation of our paper "DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera", accepted at the CVPR Workshop 2024.
See more details in [report], [paper], [certificate]
Our solution competes in MIPI 2024 Demosaic for HybridEVS Camera, achieving the BEST performance in terms of PNSR and SSIM.
Use the following command line for the installation of dependencies required to run DemosaicFormer.
git clone https://github.com/QUEAHREN/DemosaicFormer.git
cd DemosaicFormer
conda create -n pytorch181 python=3.7
conda activate pytorch181
conda install pytorch=1.8 torchvision cudatoolkit=10.2 -c pytorch
pip install matplotlib scikit-learn scikit-image opencv-python yacs joblib natsort h5py tqdm
pip install einops gdown addict future lmdb numpy pyyaml requests scipy tb-nightly yapf lpips
You can download the training set from MIPI2024 training set
Please change the path in data_preprocess.py and use command line as following to convert the bin files to RGB images:
python data_preprocess.py
- Download the model and place it in ./pretrained_models/
- We provide the preprocessd test data so that you can directly test using them. You can download the final-test sampled input.
- Please change the path in
config/test_demo.ymlat first - To test the pre-trained DemosaicFormer models of demosaicing for HybridEVS data on your own images, you can use command line as following
python test.py -opt config/test_demo.yml- Please change the path in
config/train_stage1.ymlandconfig/train_stage2.ymlat first, then execute the following commands to start training:
python -m torch.distributed.launch --nproc_per_node=6 --master_port=4321 train_mipi_pgst.py -opt config/train_stage1.yml --launcher pytorch
python -m torch.distributed.launch --nproc_per_node=6 --master_port=4321 train_mipi.py -opt config/train_stage2.yml --launcher pytorch
If you find our work useful in your research, please consider citing:
@InProceedings{Xu_2024_DemosaicFormer,
author = {Xu, Senyan and Sun, Zhijing and Zhu, Jiaying and Zhu, Yurui and Fu, Xueyang and Zha, Zheng-Jun},
title = {DemosaicFormer: Coarse-to-Fine Demosaicing Network for HybridEVS Camera},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2024},
pages = {1126-1135}
}
Should you have any question, please contact [email protected]
Acknowledgment: This code is based on the BasicSR toolbox.