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Semantic Guided Single Image Reflection Removal
Reflection is common in images capturing scenes behind a glass window, w...
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Controllable Attention for Structured Layered Video Decomposition
The objective of this paper is to be able to separate a video into its n...
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A Trip to the Moon: Personalized Animated Movies for Self-reflection
Self-tracking physiological and psychological data poses the challenge o...
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Location-aware Single Image Reflection Removal
This paper proposes a novel location-aware deep learning-based single im...
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Separate from Observation: Unsupervised Single Image Layer Separation
Unsupervised single image layer separation aims at extracting two layers...
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Video Rain/Snow Removal by Transformed Online Multiscale Convolutional Sparse Coding
Video rain/snow removal from surveillance videos is an important task in...
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Rain Removal By Image Quasi-Sparsity Priors
Rain streaks will inevitably be captured by some outdoor vision systems,...
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User-assisted Video Reflection Removal
Reflections in videos are obstructions that often occur when videos are taken behind reflective surfaces like glass. These reflections reduce the quality of such videos, lead to information loss and degrade the accuracy of many computer vision algorithms. A video containing reflections is a combination of background and reflection layers. Thus, reflection removal is equivalent to decomposing the video into two layers. This, however, is a challenging and ill-posed problem as there is an infinite number of valid decompositions. To address this problem, we propose a user-assisted method for video reflection removal. We rely on both spatial and temporal information and utilize sparse user hints to help improve separation. The key idea of the proposed method is to use motion cues to separate the background layer from the reflection layer with minimal user assistance. We show that user-assistance significantly improves the layer separation results. We implement and evaluate the proposed method through quantitative and qualitative results on real and synthetic videos. Our experiments show that the proposed method successfully removes reflection from video sequences, does not introduce visual distortions, and significantly outperforms the state-of-the-art reflection removal methods in the literature.
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