The first mobile camera phone was sold only 20 years ago, when taking pictures with one's phone was an oddity, and sharing pictures online was unheard of. Today, the smartphone is more camera than phone. How did this happen? This transformation was enabled by advances in computational photography -the science and engineering of making great images from small form factor, mobile cameras. Modern algorithmic and computing advances, including machine learning, have changed the rules of photography, bringing to it new modes of capture, post-processing, storage, and sharing. In this paper, we give a brief history of mobile computational photography and describe some of the key technological components, including burst photography, noise reduction, and super-resolution. At each step, we may draw naive parallels to the human visual system.
Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography
School of Electronic Science and Engineering, Nanjing University（南京大学电子科学与工程学院）；Nanyang Technological University（南洋理工大学）；Key Laboratory of Optoelectronic Devices and Systems with Extreme Performances of MOE, Nanjing University（南京大学极端性能光电器件与系统教育部重点实验室）
MIPI 2023 Challenge on RGBW Remosaic: Methods and Results
SenseBrain（商汤科技研究院（SenseBrain））；SenseTime Research and Tetras.AI（商汤科技研究院与Tetras.AI）；Shanghai AI Laboratory（上海人工智能实验室）；Nanyang Technological University（南洋理工大学）
Replacing Mobile Camera ISP with a Single Deep Learning Model
ETH Zurich（苏黎世联邦理工学院）
随着移动摄影的普及不断增长，现在正在投入大量精力来构建复杂的手工相机ISP解决方案。在这项工作中，我们证明了即使是最复杂的ISP管道也可以用一个端到端的深度学习模型来代替，而无需对特定设备中使用的传感器和光学器件有任何先验知识。为此，我们提出了PyNET，一种新型的金字塔CNN架构，专为细粒度图像复原而设计，隐式学习执行所有ISP步骤，如图像去马赛克，去噪，白平衡，颜色和对比度校正，去噪等。该模型经过训练，可以将直接从移动相机传感器获得的RAW Bayer数据转换为专业高端DSLR相机拍摄的照片，使解决方案独立于任何特定的移动ISP实现。为了在真实数据上验证所提出的方法，我们收集了一个大规模的数据集，其中包括1万个使用华为P20摄像机（12.3 MP Sony Exmor IMX 380传感器）和佳能5D Mark IV DSLR在野外拍摄的全分辨率RAW-RGB图像对。实验表明，所提出的解决方案可以很容易地达到嵌入式P20的ISP管道的水平，与我们的方法不同，它是结合来自两个（RGB + B/W）相机传感器的数据。本文中使用的数据集，预训练模型和代码可在项目网站上获得。
Efficient and Privacy-Protecting Background Removal for 2D Video Streaming using iPhone 15 Pro Max LiDAR
Department of Computer Science, Iowa State University, Ames, IA, USA（计算机科学系，爱荷华州立大学）；Department of Computer Science and School of Education, Iowa State University, Ames, IA, USA（计算机科学系与教育学院，爱荷华州立大学）
本研究利用iPhone 15 Pro Max的LiDAR技术，结合GPU图像处理与Metal着色器实现60帧/秒的高效背景去除，该方法不受光照影响，仅受深度数据带宽和LiDAR材质反射局限，未来分辨率提升后有望成为主流背景去除方案。
A Clonal Selection Algorithm with Levenshtein Distance based Image Similarity in Multidimensional Subjective Tourist Information and Discovery of Cryptic Spots by Interactive GHSOM
Real-Time Underwater Image Enhancement via Frequency-Guided Dual-Path Attention
Glasgow College, UESTC, Chengdu, China（格拉斯哥学院，UESTC，成都，中国）；School of Information and Communication Engineering, UESTC, Chengdu, China（信息与通信工程学院，UESTC，成都，中国）；University of Glasgow, Glasgow, UK（格拉斯哥大学，格拉斯哥，英国）
