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Towards High Fidelity Image and Video Restoration
PhD Thesis Proposal Defence Title: "Towards High Fidelity Image and Video Restoration" by Mr. Hao OUYANG Abstract: As multimedia content becomes increasingly ubiquitous in our daily lives, there is a growing demand for restoring sequences from multiple domains, as well as high-resolution images and videos from the real world. The goal of image and video restoration is to repair corrupted regions of visual data by replacing them with content that is both visually realistic and semantically consistent. This type of algorithm can be beneficial for general users in a variety of practical applications, such as removing unwanted objects, repairing facial defects, and editing data. Despite significant progress in restoration techniques, there are still issues with abrupt color artifacts and severe flickering in the resulting images or videos. The objective of the thesis is to enhance the performance of image and video restoration using internal learning techniques and flow-based models. To improve image restoration, we utilize a unique external-internal inpainting strategy with a monochromic bottleneck. Additionally, we suggest an alternative method to minimize corruption caused by editing through learning a restorable operator using quasi-invertible deep models. However, the image restoration algorithm doesn't consider temporal information and 3D structure, leading to severe flickering when applied to videos. We propose that the information propagation process can be implicitly addressed by cross-frame correlation and enforcing gradient constraints for video restoration. Furthermore, we introduce 3D representations for high-fidelity inpainting of novel views. These proposed approaches have demonstrated satisfactory performance on various image and video restoration tasks. Date: Friday, 5 May 2023 Time: 4:00pm - 6:00pm Venue: Room 5501 lifts 25/26 Committee Members: Dr. Qifeng Chen (Supervisor) Dr. Dan Xu (Chairperson) Prof. Chiew-Lan Tai Prof. Chi-Keung Tang **** ALL are Welcome ****