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
摘要
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 realworld 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 highquality 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 2804
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