论文标题
Stereoisp：重新思考双摄像头系统的图像信号处理
StereoISP: Rethinking Image Signal Processing for Dual Camera Systems
论文作者
论文摘要
传统的图像信号处理（ISP）框架旨在从单个原始测量中重建RGB图像。随着如今的多相机系统变得越来越流行，值得通过合并来自多个摄像机的原始测量值来探索ISP框架的改进。该手稿是正在开发的新ISP框架的中间进度报告，Stereoisp。它采用立体声摄像机对的原始测量结果来生成一个示例性的，降低的RGB图像，通过利用两种视图之间估计的差异。我们通过测试从立体声数据集合成的原始图像对上的性能来研究StereoISP。我们的初步结果表明，使用地面真相稀疏的差异图，在Kitti 2015上至少2DB和DrivingsTereo数据集对重建RGB图像的PSNR有所改善。
Conventional image signal processing (ISP) frameworks are designed to reconstruct an RGB image from a single raw measurement. As multi-camera systems become increasingly popular these days, it is worth exploring improvements in ISP frameworks by incorporating raw measurements from multiple cameras. This manuscript is an intermediate progress report of a new ISP framework that is under development, StereoISP. It employs raw measurements from a stereo camera pair to generate a demosaicked, denoised RGB image by utilizing disparity estimated between the two views. We investigate StereoISP by testing the performance on raw image pairs synthesized from stereo datasets. Our preliminary results show an improvement in the PSNR of the reconstructed RGB image by at least 2dB on KITTI 2015 and drivingStereo datasets using ground truth sparse disparity maps.
