Dissertation/ Thesis
Low-level vision processing : new approaches and sensors
| Τίτλος: | Low-level vision processing : new approaches and sensors |
|---|---|
| Συγγραφείς: | Wang, Zhouxia, 王州霞 |
| Στοιχεία εκδότη: | The University of Hong Kong (Pokfulam, Hong Kong) |
| Έτος έκδοσης: | 2023 |
| Συλλογή: | University of Hong Kong: HKU Scholars Hub |
| Θεματικοί όροι: | Image processing - Data processing |
| Περιγραφή: | Low-level Vision processing aims to pixel-wisely process low-quality vision data, such as images and videos, to attain their high-quality ones. Low-level vision processing is complex since it contains a wide variety of low-quality data and involves many scenarios. In this thesis, we study low-level vision processing in three kinds of scenarios: scenarios with human faces only, natural scenarios, and an extremely challenging scenario. For each scenario, we delicately design a corresponding approach according to the property of the unprocessed data and scenarios to attain high-quality processing results. Our studies of scenarios with human faces only mainly focus on blind face restoration. First, we propose a RestoreFormer++ for blind face image restoration. It introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, achieving high-quality face images with both realness and fidelity. Its priors are matched from a learned reconstruction-oriented high-quality dictionary which is more accordant to the face restoration task, leading to rich details in the restored face images. Moreover, it is more robust and general to real-world degradation since its well-designed extending degrading model alleviates the synthetic-to-real-world gap. Then, we extend our study to face video restoration. We systematically analyze the potential benefits and difficulties posed by current face image restoration algorithms when extended to real-world face video restoration and provide a viable solution to mitigate the analyzed difficulties. Our study of natural scenarios is image deblurring. In this work, we introduce an event-based vision sensor, which can detect per-pixel brightness changes in microsecond resolution. Considering the complementary between the intensity images captured with a frame-based camera and event data captured with an event camera in temporal and spatial aspects, we propose to alternately enhance the quality of intensity image and even data with a DeblurNet ... |
| Τύπος εγγράφου: | doctoral or postdoctoral thesis |
| Γλώσσα: | English |
| Relation: | HKU Theses Online (HKUTO); 991044736606203414; https://hub.hku.hk/handle/10722/335162 |
| Διαθεσιμότητα: | https://hub.hku.hk/handle/10722/335162 |
| Rights: | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. ; This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Αριθμός Καταχώρησης: | edsbas.C19E99BA |
| Βάση Δεδομένων: | BASE |
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