An End-to-End Real-World Camera Imaging Pipeline

TL;DR

This paper introduces RealCamNet, an end-to-end neural camera imaging pipeline that jointly optimizes image processing, restores optical distortions, and compresses images, outperforming traditional methods in real-world scenarios.

Contribution

The paper presents a novel end-to-end architecture for camera imaging that restores distortions and compresses images, along with a new dataset for training and evaluation.

Findings

RealCamNet achieves superior rate-distortion performance.

It has lower inference latency than existing methods.

The dataset improves training for real-world imaging pipelines.

Abstract

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.