Deep Learning-based Light Field Reconstruction and Processing
Speakers: Dr. Junhui Hou, and Ms. Jing Jin, Department of Computer Science, City University of Hong Kong
The light field describes the radiance of the light rays permeating the 3D free space as a function of their positions and directions. The light field image can be interpreted as a series of 2D images observed from different viewpoints, which implicitly encodes the depth information of the 3D scene. A high-quality 4D light field image records rich information of the scene in both appearance and geometry, and thus, enables worldwide applications in the fields of computer graphics and computer vision, such as novel view rendering, post-capture refocusing, scene reconstruction, and virtual/augmented reality. However, the high dimensionality of light field data also raises great challenges for the acquisition and processing compared with conventional 2D images. Therefore, recent researchers take advantage of the advanced deep learning techniques to explore the intrinsic characteristics of light field data.
This tutorial will introduce the basic knowledge of the light field and then focus on deep-learning-based light field reconstruction and processing algorithms. We will start with theoretical descriptions about the light field function and its basic applications, including light field rendering and post-capture re-focusing, followed by the introduction of typical light field acquisition approaches, including multi-sensor, multi-exposure, and multiplexing capture. Next, we will comprehensively overview computational approaches to reconstruct the high-quality 4D light field image from low-cost inputs that are sparsely sampled in spatial or angular domains. Finally, we will introduce important techniques of light field processing, including depth estimation and compression, which are necessary intermediate steps for subsequent light field-based applications.
Biography of speakers:
Dr. Hou was the recipient of several prestigious awards, including the Chinese Government Award for Outstanding Students Study Abroad from China Scholarship Council in 2015 and the Early Career Award (3/381) from the Hong Kong Research Grants Council in 2018. He is an elected member of MSA-TC and VSPC-TC, IEEE CAS. He is currently an Associate Editor for IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology, Signal Processing: Image Communication, and The Visual Computer. He also served as the Guest Editor for the IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and an Area Chair of ACM MM’19/20/21, IEEE ICME’20, VCIP’20/21, and WACV’21.
Multimedia Information Security, Forensic and Privacy
Speakers: Prof. Chi-Man Pun, Faculty of Science and Technology, University of Macau
Manipulating multimedia content has become much easier because of the rapid development of multimedia editing software on computers and smartphones. Various multimedia tampering operations, such as splicing, object removal, and copy-move, are applied to modify the multimedia contents easily. Besides, many powerful post-processing methods are proposed to conceal the noticeable manipulation traces. However, the abuse of forgery multimedia, such as Deepfake, has hugely affected our lives and harms the media trust. Besides, live streaming techniques, like TikTok, YouTube, Facebook, etc., endow people with the capability to instantly record and broadcast real scenes to audiences. Without the imposed censorship, these private streaming channels severely disregard personal privacy rights. Therefore, the research works on multimedia information security, forensic, and privacy have been becoming more and more important in recent years. In this tutorial, the recent advances of approaches for multimedia information security, forensic detection methods, and privacy protection will be introduced and discussed.
Biography of speakers: