ParamISP：利用相机参数学习前向与逆向ISP
ParamISP: Learned Forward and Inverse ISPs using Camera Parameters
AI总结：
针对现有基于学习的ISP方法难以适配相机参数变化的问题，本文提出ParamISP方法，通过ParamNet模块利用EXIF中的相机参数控制ISP网络，实现sRGB与RAW图像的双向高精度转换，可应用于多种图像处理任务。
摘要：
RAW图像很少被共享，主要原因是与相机ISP（图像信号处理器）生成的sRGB图像相比，其数据量过大。近期已有研究实现了对相机ISP前向与逆向过程的学习，使得在输入sRGB图像上开展具有物理意义的RAW级图像处理成为可能。然而，现有基于学习的ISP方法无法应对ISO、曝光时间等相机参数带来的ISP过程大幅差异，在各类应用中存在局限性。本文提出ParamISP——一种用于sRGB与RAW图像之间前向和逆向转换的基于学习的方法，它采用一种名为ParamNet的新型神经网络模块来利用相机参数。给定EXIF数据中提供的相机参数，ParamNet将其转换为特征向量以控制ISP网络。大量实验表明，与现有方法相比，ParamISP在RAW和sRGB重建上取得了更优的结果，且可有效应用于去模糊数据集合成、RAW去模糊、HDR重建以及相机间迁移等多种场景。
英文摘要：
RAW images are rarely shared mainly due to its excessive data size compared to their sRGB counterparts obtained by camera ISPs. Learning the forward and inverse processes of camera ISPs has been recently demonstrated, enabling physically-meaningful RAW-level image processing on input sRGB images. However, existing learning-based ISP methods fail to handle the large variations in the ISP processes with respect to camera parameters such as ISO and exposure time, and have limitations when used for various applications. In this paper, we propose ParamISP, a learning-based method for forward and inverse conversion between sRGB and RAW images, that adopts a novel neural-network module to utilize camera parameters, which is dubbed as ParamNet. Given the camera parameters provided in the EXIF data, ParamNet converts them into a feature vector to control the ISP networks. Extensive experiments demonstrate that ParamISP achieve superior RAW and sRGB reconstruction results compared to previous methods and it can be effectively used for a variety of applications such as deblurring dataset synthesis, raw deblurring, HDR reconstruction, and camera-to-camera transfer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
