文献类型：期刊文献

英文题名：Conditional GAN-Based Two-Stage ISP Tuning Method: A Reconstruction-Enhancement Proxy Framework

作者：Zhan, Pengfei[1];Ye, Jiongyao[1]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China

年份：2025

卷号：15

期号：6

外文期刊名：APPLIED SCIENCES-BASEL

收录：;EI(收录号：20251318136186);WOS:【SCI-EXPANDED(收录号:WOS:001453539500001)】；

语种：英文

外文关键词：image signal processor; proxy tuning; parameter optimization

摘要：Image signal processing (ISP), a critical component in camera imaging, has traditionally relied on experience-driven parameter tuning. This approach suffers from inefficiency, fidelity issues, and conflicts with visual enhancement objectives. This paper introduces ReEn-GAN, an innovative staged ISP proxy tuning framework. ReEn-GAN decouples the ISP process into two distinct stages: reconstruction (physical signal recovery) and enhancement (visual quality and color optimization). By employing distinct network architectures and loss functions tailored to specific objectives, the two-stage proxy can effectively optimize both the reconstruction and enhancement modules within the ISP pipeline. Compared to tuning with an end-to-end proxy network, the proposed method's proxy more effectively extracts hierarchical information from the ISP pipeline, thereby mitigating the significant changes in image color and texture that often result from parameter adjustments in an end-to-end proxy model. This paper conducts experiments on image denoising and object detection tuning tasks, and compares the performance of the two types of proxies. The results demonstrate that the proposed method outperforms end-to-end proxy methods on public datasets (SIDD, KITTI) and achieves over 21% improvement in performance metrics compared to hand-tuning methods.