Intelligent Image Enhancement and Restoration - from Prior Driven Model to Advanced Deep Learning

Intelligent image/video editing is a fundamental topic in image processing which has witnessed rapid progress in the last two decades. Due to various degradations in the image and video capturing, transmission and storage, image and video include many undesirable effects, such as low resolution, low light condition, rain streak and rain drop occlusions. The recovery of these degradations is ill-posed. With the wealth of statistic-based methods and learning-based methods, this problem can be unified into the cross-domain transfer, which cover more tasks, such as image stylization.

In our tutorial, we discussed recent progresses of image stylization, rain streak/drop removal, image/video super-resolution, and low light image enhancement. This tutorial covers both traditional statistics based and deep-learning based methods, and contains both biological-driven model, i.e. Retinex model, and data-driven model. An image processing viewpoint that considers the popular deep networks as a traditional Maximum-a-Posteriori (MAP) Estimation is provided. The side priors, designed by researchers and learned by multi-task learnings, and automatically learned priors, captures by adversarial learning are two kinds of important priors in this framework. Three works under this framework, including single image super-resolution, low light image enhancement, and single image raindrop removal are presented.

- Deep Joint Rain Detection and Removal From a Single Image
- Joint Rain Detection and Removal from a Single Image with Contextualized Deep Networks
- Erase or Fill? Deep Joint Recurrent Rain Removal and Reconstruction in Videos
- D3R-Net: Dynamic Routing Residue Recurrent Network for Video Rain Removal
- Attentive Generative Adversarial Network for Raindrop Removal from A Single Image

Slide: Baiduyun Link (password: 4h1j)

- Awesome Typography: Statistics-Based Text Effects Transfer
- Context-Aware Unsupervised Text Stylization
- Text Effects Transfer via Stylization and Destylization

Slide: Baiduyun Link (password: z6nd)

- Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
- Video Super-Resolution Based on Spatial-Temporal Recurrent Residual Networks

Slide: Baiduyun Link (password: q27w)

- Structure-Revealing Low-Light Image Enhancement Via Robust Retinex Model
- Joint Enhancement and Denoising Method via Sequential Decomposition
- Deep Retinex Decomposition for Low-Light Enhancement

Slide: Baiduyun Link (password: b3g7)

Jiaying Liu , Peking University, Beijing, China

Jiaying Liu is currently an Associate Professor with the Institute of Computer Science and Technology, Peking University. She received the Ph.D. degree (Hons.) in computer science from Peking University, Beijing China, 2010. She has authored over 100 technical articles in refereed journals and proceedings, and holds 34 granted patents. Her current research interests include multimedia signal processing, compression, and computer vision.

Dr. Liu is a Senior Member of IEEE and CCF. She was a Visiting Scholar with the University of Southern California, Los Angeles, from 2007 to 2008. She was a Visiting Researcher with the Microsoft Research Asia in 2015 supported by the Star Track Young Faculties Award. She has served as a member of Multimedia Systems & Applications Technical Committee (MSA-TC), Visual Signal Processing and Communications Technical Committee (VSPC) and Education and Outreach Technical Committee (EO-TC) in IEEE Circuits and Systems Society, a member of the Image, Video, and Multimedia (IVM) Technical Committee in APSIPA. She has also served as the Technical Program Chair of IEEE VCIP-2019/ACM ICMR-2021, the Publicity Chair of IEEE ICIP-2019/VCIP-2018, the Grand Challenge Chair of IEEE ICME-2019, and the Area Chair of ICCV-2019. She was the APSIPA Distinguished Lecturer (2016-2017).

In addition, Dr. Liu also devotes herself to teaching. She has run MOOC Programming Courses via Coursera/edX/ChineseMOOCs, which have been enrolled by more than 60 thousand students. She is also the organizer of the first Chinese MOOC Specialization in Computer Science. She is the youngest recipient of Peking University Outstanding Teaching Award.

Wenhan Yang, City University of Hong Kong, Hong Kong

Wenhan Yang is a Postdoc research fellow with the Department of Computer Science, City University of Hong Kong. Wenhan Yang received the B.S degree and Ph.D. degree (Hons.) in computer science from Peking University, Beijing, China, in 2012 and 2018. Dr. Yang was a Visiting Scholar with the National University of Singapore, from 2015 to 2016. He has authored over 30 technical articles in refereed journals and proceedings. His current research interests include deep-learning based image processing, bad weather restoration, related applications and theories.