@inproceedings{
xu2024an,
title={An End-to-End Real-World Camera  Imaging Pipeline},
author={Kepeng Xu and Zijia Ma and Li Xu and Gang He and Yunsong Li and Wenxin Yu and Taichu Han and Cheng Yang},
booktitle={ACM Multimedia 2024},
year={2024},
url={https://openreview.net/forum?id=q1AQmI4VQx}
}
Recent advances in neural camera imaging pipelines have demonstrated notable progress. Nevertheless, the real-world imaging pipeline still faces challenges including the lack of joint optimization in system components, computational redundancies, and optical distortions such as lens shading. In light of this, we propose an end-to-end camera imaging pipeline (RealCamNet) to enhance real-world camera imaging performance. Our methodology diverges from conventional, fragmented multi-stage image signal processing towards end-to-end architecture. This architecture facilitates joint optimization across the full pipeline and the restoration of coordinate-biased distortions. RealCamNet is designed for high-quality conversion from RAW to RGB and compact image compression. Specifically, we deeply analyze coordinate-dependent optical distortions, e.g., vignetting and dark shading, and design a novel Coordinate-Aware Distortion Restoration (CADR) module to restore coordinate-biased distortions. Furthermore, we propose a Coordinate-Independent Mapping Compression (CIMC) module to implement tone mapping and redundant information compression. Existing datasets suffer from misalignment and overly idealized conditions, making them inadequate for training real-world imaging pipelines. Therefore, we collected a real-world imaging dataset. Experiment results show that RealCamNet achieves the best rate-distortion performance with lower inference latency.
An End-to-End Real-World Camera  Imaging Pipeline
Kepeng Xu Zijia Ma Li Xu Gang He Yunsong Li Wenxin Yu Taichu Han Cheng Yang