【ICCV2023教程】了解相机内部渲染流程及AI/深度学习的角色，267页ppt

这个教程旨在为参与者提供现代数字相机工作方式的详细概述。教程分为两部分组织。第一部分提供了色彩理论和颜色表示的背景，即常见于计算机视觉中的CIE 1931 XYZ色彩空间及其衍生物（sRGB，L*ab，Yuv等）。教程的第一部分还将讨论数字相机的常规图像信号处理（ISP）硬件上用于将低级传感器原始RGB响应转换为其最终标准RGB（sRGB）颜色的常规方法。这些方法包括计算色彩恒定性（自动白平衡）、色度转换、图像解马赛克、图像降噪、色调映射、超分辨率和一般颜色处理。本教程的第二部分讨论了旨在改善单个ISP组件的最新AI方法。接下来是旨在完全用基于AI的ISP替代常规ISP的当前AI方法。 Books

R.W.G. Hunt, The Reproduction of Colour, Wiley , 2004 G. Sharma, Digital Color Imaging Handbook, CRC Press , 2003 M. Fairchild, Color Appearance Models, Wiley , 2005 D. Forsyth and J. Ponce, Computer Vision: A modern approach, Prentice Hall, 2011 P. Green and L. MacDonlad, Colour Engineering: Achieving Device Independent Colour, Wiley , 2002

Articles/Conference Papers Jeong W. and Jung S.W. "RAWToBit: A Fully End-to-end Camera ISP Network", ECCV'22 Umn K.H. et al. "Image Compression-Aware Deep Camera ISP Network", IEEE Access'21 Liu et al. "Deep-FlexISP: A Three-stage Framework for Night Photography Rendering", CVPRW'22 (NTIRE) Ershov et al. "NTIRE 2022 Challenge on Night Photography Rendering", CVPRW'22 (NTIRE) Liang Z. et al. "CameraNet: A Two-Stage Frameworkfor Effective Camera ISP Learning", TIP'21 Souza M. and Heidrich W. "CRISPnet: Color Rendition ISP Net", arxiv'21 Chen C. et al. "Learning to See In the Dark", CVPR'18 Ignatov A. et al. "Replacing Mobile Camera ISP with a Single Deep Learning Model", CVPRW'19 (NTIRE)