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|Title:||Single-image reflection removal : from computational imaging to deep learning||Authors:||Wan, Renjie||Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||Wan, R. (2018). Single-image reflection removal : from computational imaging to deep learning. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||Reflection removal aims at enhancing the visibility of the background scene while removing the reflections for images taken through the transparent glass. Though it is of broad application to various computer vision tasks, it is very challenging due to its ill-posed nature and additional priors are needed to make this problem tractable. Traditional reflection removal methods solve this problem by making use of different heuristic observations or assumptions. These assumptions are seldom satisfied in practical scenarios. In this thesis, we generalize the assumptions for the reflection removal problems by using different information or imposing new constraints. We first propose a method by exploring the blur inconsistency between the background and reflections. Then, we introduce the first benchmark dataset in this area and analyze limitations of existing methods based on this dataset. In the third work, we address this problem by using the sparsity prior and non-local image prior from the external source. Then, with the observation that most reflections only cover a part of the whole image, we propose a method to automatically detect the regions with and without reflections and process them in a heterogeneous manner. At last, we introduce a data-driven method by using the concurrent deep learning framework. Our methods have been evaluated by using the benchmark dataset proposed in our second work. These evaluations cover a diversity of common scenarios in our daily life; hence the experiments prove that our approaches are valid for a broad class of practical scenarios. The main contributions of this thesis are three folds: We thoroughly study the reflection properties observed in our daily scenarios; we propose the first benchmark evaluation dataset in this area and use it to analyze the limitations of existing methods; we propose various approaches to solve this problem from different angles. The efforts and achievements in this thesis promote the practical capabilities of reflection removal techniques and provide fundamental support for future researches.||URI:||https://hdl.handle.net/10356/82986
|DOI:||10.32657/10220/47556||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||IGS Theses|
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